Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Integral curves driven by second eigenvector of Hessian Qiao and WP (2015); dimension d = 2. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Integral curves driven by second eigenvector of Hessian Qiao and WP (2015); dimension d = 2. driving vector field: v ( x ) = second eigenvector of Hessian. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Integral curves driven by second eigenvector of Hessian Qiao and WP (2015); dimension d = 2. driving vector field: v ( x ) = second eigenvector of Hessian. Motivation: Filament (ridge line) estimation. More later. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! direct estimates excess mass approach: Hartigan (1987), M¨ uller and Sawitzki (1991), Nolan (1991), WP (1995) minimum volume sets: classical concept; shorth (Lientz, 1970, Andrews et al. 1972) set estimation: Scott et al. (2006), Walther (1997), WP (1997) volume (length) of MV-sets: generalized quantiles (Gr¨ ubel, 1988; Einmahl and Mason, 1992; WP 1997) plug-in approach via kernel density estimation: Baillo et al. (2000), Cuevas et al. (2001, 2006, 2007, 2009), Cadre (2006), Scott et al. (2006), Mason and WP (2009), Rigollet and Vert (2009), Bouka et al. (2015). . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! direct estimates excess mass approach: Hartigan (1987), M¨ uller and Sawitzki (1991), Nolan (1991), WP (1995),. . . minimum volume sets: classical concept; shorth (Lientz, 1970, Andrews et al. 1972) set estimation: Scott et al. (2006), Walther (1997), WP (1997) volume (length) of MV-sets: generalized quantiles (Gr¨ ubel, 1988; Einmahl and Mason, 1992; WP 1997) plug-in approach via kernel density estimation: Baillo et al. (2000), Cuevas et al. (2001, 2006, 2007, 2009), Cadre (2006), Scott et al. (2006), Mason and WP (2009), Rigollet and Vert (2009), Bouka et al. (2015). . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! direct estimates excess mass approach: Hartigan (1987), M¨ uller and Sawitzki (1991), Nolan (1991), WP (1995),. . . minimum volume sets: classical concept; shorth (Lientz, 1970, Andrews et al. 1972) set estimation: Scott et al. (2006), Walther (1997), WP (1997) volume (length) of MV-sets: generalized quantiles (Gr¨ ubel, 1988; Einmahl and Mason, 1992; WP 1997) plug-in approach via kernel density estimation: Baillo et al. (2000), Cuevas et al. (2001, 2006, 2007, 2009), Cadre (2006), Scott et al. (2006), Mason and WP (2009), Rigollet and Vert (2009), Bouka et al. (2015). . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! direct estimates excess mass approach: Hartigan (1987), M¨ uller and Sawitzki (1991), Nolan (1991), WP (1995),. . . minimum volume sets: classical concept; shorth (Lientz, 1970, Andrews et al. 1972) set estimation: Scott et al. (2006), Walther (1997), WP (1997) volume (length) of MV-sets: generalized quantiles (Gr¨ ubel, 1988; Einmahl and Mason, 1992; WP 1997) plug-in approach via kernel density estimation: Baillo et al. (2000), Cuevas et al. (2001, 2006, 2007, 2009), Cadre (2006), Scott et al. (2006), Mason and WP (2009), Rigollet and Vert (2009), Bouka et al. (2015). . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! direct estimates excess mass approach: Hartigan (1987), M¨ uller and Sawitzki (1991), Nolan (1991), WP (1995),. . . minimum volume sets: classical concept; shorth (Lientz, 1970, Andrews et al. 1972) set estimation: Scott et al. (2006), Walther (1997), WP (1997) volume (length) of MV-sets: generalized quantiles (Gr¨ ubel, 1988; Einmahl and Mason, 1992; WP 1997) plug-in approach via kernel density estimation: Baillo et al. (2000), Cuevas et al. (2001, 2006, 2007, 2009), Cadre (2006), Scott et al. (2006), Mason and WP (2009), Rigollet and Vert (2009), Bouka et al. (2015). . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! direct estimates excess mass approach: Hartigan (1987), M¨ uller and Sawitzki (1991), Nolan (1991), WP (1995),. . . minimum volume sets: classical concept; shorth (Lientz, 1970, Andrews et al. 1972) set estimation: Scott et al. (2006), Walther (1997), WP (1997) volume (length) of MV-sets: generalized quantiles: Gr¨ ubel (1988); Einmahl and Mason (1992); WP (1997) plug-in approach via kernel density estimation: Baillo et al. (2000), Cuevas et al. (2001, 2006, 2007, 2009), Cadre (2006), Scott et al. (2006), Mason and WP (2009), Rigollet and Vert (2009), Bouka et al. (2015). . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! direct estimates excess mass approach: Hartigan (1987), M¨ uller and Sawitzki (1991), Nolan (1991), WP (1995),. . . minimum volume sets: classical concept; shorth (Lientz, 1970, Andrews et al. 1972) set estimation: Scott et al. (2006), Walther (1997), WP (1997) volume (length) of MV-sets: generalized quantiles: Gr¨ ubel (1988); Einmahl and Mason (1992); WP (1997) plug-in approach via kernel density estimation: Baillo et al. (2000), Cuevas et al. (2001, 2006, 2007, 2009), Cadre (2006), Scott et al. (2006), Mason and WP (2009), Rigollet and Vert (2009), Bouka et al. (2015). . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation of level sets Level sets of a function f : R d → R are given by � � x ∈ R d : f ( x ) ≥ λ = f − 1 [ λ, ∞ ] . Γ f ( λ ) = Note: regularity � boundaries of level sets f − 1 ( λ ) are integral curves! direct estimates excess mass approach: Hartigan (1987), M¨ uller and Sawitzki (1991), Nolan (1991), WP (1995),. . . minimum volume sets: classical concept; shorth (Lientz, 1970, Andrews et al. 1972) set estimation: Scott et al. (2006), Walther (1997), WP (1997) volume (length) of MV-sets: generalized quantiles: Gr¨ ubel (1988), Einmahl and Mason (1992), WP (1997) plug-in approach via kernel density estimation: Baillo et al. (2000), Cuevas et al. (2001, 2006, 2007, 2009), Cadre (2006), Scott et al. (2006), Mason and WP (2009), Rigollet and Vert (2009), Bouka et al. (2015). . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Confidence regions for density level sets Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Confidence regions for density level sets X 1 , . . . , X n ∼ f . Fix λ > 0 and γ ∈ [0 , 1]. Goal: Find region � C n with P ( f − 1 ( λ ) ⊂ � C n ) → γ . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Confidence regions for density level sets X 1 , . . . , X n ∼ f . Fix λ > 0 and γ ∈ [0 , 1]. Goal: Find region � C n with P ( f − 1 ( λ ) ⊂ � C n ) → γ . Two different approaches in literature, based on vertical variation horizontal variation Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Confidence regions for density level sets X 1 , . . . , X n ∼ f . Fix λ > 0 and γ ∈ [0 , 1]. Goal: Find region � C n with P ( f − 1 ( λ ) ⊂ � C n ) → γ . Two different approaches in literature, based on vertical variation horizontal variation Both approaches are based on kernel density estimation: � n � X i − x � Let � 1 f n ( x ) = i =1 K , and nh d h � � x ∈ R d : � Γ b f ( λ ) = f n ( x ) ≥ λ . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Vertical variation Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Vertical variation Construct confidence region of the form � � � f ( λ + β n ) = � f − 1 C n = Γ b f ( λ − β n ) \ Γ b λ − β n , λ + β n . n Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Vertical variation Construct confidence region of the form � � � f ( λ + β n ) = � f − 1 C n = Γ b f ( λ − β n ) \ Γ b λ − β n , λ + β n . n Question: How to find an appropriate value of β n ? Idea: Use γ -quantile of distribution of sup x ∈ f − 1 ( λ ) | � f n ( x ) − f ( x ) | , Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Vertical variation Construct confidence region of the form � � � f ( λ + β n ) = � f − 1 C n = Γ b f ( λ − β n ) \ Γ b λ − β n , λ + β n . n Question: How to find an appropriate value of β n ? Idea: Use γ -quantile of distribution of sup x ∈ f − 1 ( λ ) | � f n ( x ) − f ( x ) | , because � � f − 1 ( λ ) ⊂ � f − 1 λ − β n , λ + β n n ⇔ − β n ≤ � for all x ∈ f − 1 ( λ ) f n ( x ) − λ ≤ β n Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Vertical variation One might consider two approximations of distribution of sup x ∈ f − 1 ( λ ) | � f n ( x ) − f ( x ) | : bootstrap large sample (cf. Qiao and WP, 2015). Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Vertical variation One might consider two approximations of distribution of sup x ∈ f − 1 ( λ ) | � f n ( x ) − f ( x ) | : bootstrap large sample (cf. Qiao and WP, 2015). Mammen and WP (2013) use related approach and construct bootstrap approximation of sup x ∈ f − 1 [ λ − b n ,λ + b n ] | � f n ( x ) − f ( x ) | , for appropriately chosen sequence b n → 0 . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Horizontal variation Chen et al. (2015a), Qiao and WP (2015) Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Horizontal variation Chen et al. (2015a), Qiao and WP (2015) Simple relation: At a given point x ∈ f − 1 ( λ ), | � f n ( x ) − f ( x ) | ≈ � grad f ( x ) � , d ( x , � f − 1 ( λ )) n Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Horizontal variation Chen et al. (2015a), Qiao and WP (2015) Simple relation: At a given point x ∈ f − 1 ( λ ), | � f n ( x ) − f ( x ) | ≈ � grad f ( x ) � , d ( x , � f − 1 ( λ )) n where d ( x , � f − 1 ( λ )) = inf y ∈ b ( λ ) d ( x , y ) . f − 1 n n Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Horizontal variation Chen et al. (2015a), Qiao and WP (2015) Simple relation: At a given point x ∈ f − 1 ( λ ), | � f n ( x ) − f ( x ) | ≈ � grad f ( x ) � , d ( x , � f − 1 ( λ )) n where d ( x , � f − 1 ( λ )) = inf y ∈ b ( λ ) d ( x , y ) . In other words, f − 1 n n | � f n ( x ) − f ( x ) | ≈ d ( x , � f − 1 ( λ )) n � grad f ( x ) � Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Horizontal variation Chen et al. (2015a), Qiao and WP (2015) Simple relation: At a given point x ∈ f − 1 ( λ ), | � f n ( x ) − f ( x ) | ≈ � grad f ( x ) � , d ( x , � f − 1 ( λ )) n where d ( x , � f − 1 ( λ )) = inf y ∈ b ( λ ) d ( x , y ) . In other words, f − 1 n n | � f n ( x ) − f ( x ) | ≈ d ( x , � f − 1 ( λ )) n � grad f ( x ) � Uniform control of | b f n ( x ) − f ( x ) | � grad f ( x ) � Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Horizontal variation Chen et al. (2015a), Qiao and WP (2015) Simple relation: At a given point x ∈ f − 1 ( λ ), | � f n ( x ) − f ( x ) | ≈ � grad f ( x ) � , d ( x , � f − 1 ( λ )) n where d ( x , � f − 1 ( λ )) = inf y ∈ b ( λ ) d ( x , y ) . In other words, f − 1 n n | � f n ( x ) − f ( x ) | ≈ d ( x , � f − 1 ( λ )) n � grad f ( x ) � Uniform control of | b f n ( x ) − f ( x ) | � grad f ( x ) � � control of Hausdorff distance Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Horizontal variation Chen et al. (2015a), Qiao and WP (2015) Simple relation: At a given point x ∈ f − 1 ( λ ), | � f n ( x ) − f ( x ) | ≈ � grad f ( x ) � , d ( x , � f − 1 ( λ )) n where d ( x , � f − 1 ( λ )) = inf y ∈ b ( λ ) d ( x , y ) . In other words, f − 1 n n | � f n ( x ) − f ( x ) | ≈ d ( x , � f − 1 ( λ )) n � grad f ( x ) � Uniform control of | b f n ( x ) − f ( x ) | � grad f ( x ) � � control of Hausdorff distance � confidence regions by using quantiles of Hausdorff distance Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Horizontal variation Chen et al. (2015a), Qiao and WP (2015) Simple relation: At a given point x ∈ f − 1 ( λ ), | � f n ( x ) − f ( x ) | ≈ � grad f ( x ) � , d ( x , � f − 1 ( λ )) n where d ( x , � f − 1 ( λ )) = inf y ∈ b ( λ ) d ( x , y ) . In other words, f − 1 n n | � f n ( x ) − f ( x ) | ≈ d ( x , � f − 1 ( λ )) n � grad f ( x ) � Uniform control of | b f n ( x ) − f ( x ) | � grad f ( x ) � � control of Hausdorff distance � confidence regions by using quantiles of Hausdorff distance � � d H ( f − 1 ( λ ) , � f − 1 d ( x , � f − 1 d ( x , f − 1 ( λ )) ( λ )) = max sup ( λ )) , sup . n n x ∈ f − 1 ( λ ) x ∈ b f − 1 ( λ ) n Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Inference for modes / modal clustering Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Inference for modes / modal clustering - (local) level sets � modal regions Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Inference for modes / modal clustering - (local) level sets � modal regions - geometric properties of level sets � number of modes Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Inference for modes / modal clustering - (local) level sets � modal regions - geometric properties of level sets � number of modes - geometric properties of level sets � capture features of density � visualization (level set tree) Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Inference for modes / modal clustering - (local) level sets � modal regions - geometric properties of level sets � number of modes - geometric properties of level sets � capture features of density � visualization (level set tree) - excess mass approach, Hartigan’s dip � testing for modes Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Inference for modes / modal clustering - (local) level sets � modal regions - geometric properties of level sets � number of modes - geometric properties of level sets � capture features of density � visualization (level set tree) - excess mass approach, Hartigan’s dip � testing for modes - integral curves driven by gradient fields � modal clustering Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Inference for modes / modal clustering - (local) level sets � modal regions - geometric properties of level sets � number of modes - geometric properties of level sets � capture features of density � visualization (level set tree) - excess mass approach, Hartigan’s dip � testing for modes - integral curves driven by gradient fields � modal clustering - existence of antimodes � testing for modes Hartigan (1975, 1985, 1987, 2000); M¨ uller and Sawitzki (1991); WP (1995); Burman & WP (2009); Chac´ on (2013), Chen et al. (2015b) Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation and inference for persistent homology: TDA Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation and inference for persistent homology: TDA Target: topological properties of supports and more general of level sets (Bobrowski et al. 2015); measured by ranks of homology groups Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation and inference for persistent homology: TDA Target: topological properties of supports and more general of level sets (Bobrowski et al. 2015); measured by ranks of homology groups Estimate homologies of a filtration based on simplicial complexes built on data (filtration based on level sets); Betti numbers (often: β 0 - number of connected components) Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation and inference for persistent homology: TDA Target: topological properties of supports and more general of level sets (Bobrowski et al. 2015); measured by ranks of homology groups Estimate homologies of a filtration based on simplicial complexes built on data (filtration based on level sets); Betti numbers (often: β 0 - number of connected components) Distinguish between signal and noise by using persistency . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Estimation and inference for persistent homology: TDA Target: topological properties of supports and more general of level sets (Bobrowski et al. 2015); measured by ranks of homology groups Estimate homologies of a filtration based on simplicial complexes built on data (filtration based on level sets); Betti numbers (often: β 0 - number of connected components) Distinguish between signal and noise by using persistency . Bubenik and Kim (2006); Balakrishnan et al. (2011, 2013); Chazal et al. (2014a,b), Fasy et al. (2013); Bauer et al. (2014), Bobrowski et al. (2015), Boissonat et al. (2015), . . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Filament or ridge line estimation Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Filament or ridge line estimation • What is a filament? Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Filament or ridge line estimation • What is a filament? Definition: A point is said to be a ridge point or a filament point if λ 2 < 0 H ∇ f = λ 1 ∇ f where λ 1 > λ 2 are the two eigenvalues of the Hessian H ( x ). A filament consists of filament points and is an integral curve of the gradient. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Filament or ridge line estimation • What is a filament? Definition: A point is said to be a ridge point or a filament point if λ 2 < 0 H ∇ f = λ 1 ∇ f where λ 1 > λ 2 are the two eigenvalues of the Hessian H ( x ). A filament consists of filament points and is an integral curve of the gradient. Let V ( x ) denote second eigenvector of Hessian H . On the filament, either ∇ f = 0 or ∇ f � V ⊥ , i.e. �∇ f , V � = 0. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament ¡ ¡ From Chen et al. (2014). Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Geometric idea �∇ f ( x ) , V ( x ) � and V ( x ) T ∇ 2 f ( x ) V ( x ) = λ 2 ( x ) � V ( x ) � 2 are first and second order directional derivative of f ( x ) along V ( x ). Thus filament points are local mode of f ( x ) along the direction V ( x ). Geometric idea : Consider vector field generated by the second eigenvectors V ( x ) of the Hessian H of f . • A ridge point corresponds to a local mode of f along the path of the corresponding integral curve for the vector field generated by V ( x ). Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Application areas Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Application areas seismology: analysis of fault lines analysing road or river networks cosmology: cosmic web medical imaging: e.g. blood vessels network Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Related literature Minimum spanning tree, Barrow et al. (1985) Candy model, Stoica et al. (2005) Principal curves ; Hastie and Stuetzle (1989), Kegl et al. (2000), Sandilya and Kulkarni (2002), and Smola et al. (2001) Local principal curve; Einbeck, Tutz and Evers (2005), Einbeck, Evers, and Bailer-Jones (2007) Skeleton; Novikov et al. (2006) Nonparametric penalized maximum likelihood; Tibshirani (1992) Beamlets; Donoho and Huo (2002), Arias-Castro et al. (2006) feature detection in point clouds (Engineering/CS): e.g. Weber et al. (2006), Daniels et al. (2007) . . . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Related other concepts Conceptually related to other statistical concepts: mode hunting Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Related other concepts Conceptually related to other statistical concepts: mode hunting integral curve estimation Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Related other concepts Conceptually related to other statistical concepts: mode hunting integral curve estimation tracking fault lines (Hall and Rau, 2000); Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Related other concepts Conceptually related to other statistical concepts: mode hunting integral curve estimation tracking fault lines (Hall and Rau, 2000); principal curves (Hastie and Stuetzle, 1989, Sandilya and Kukarni, 2002); Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Related other concepts Conceptually related to other statistical concepts: mode hunting integral curve estimation tracking fault lines (Hall and Rau, 2000); principal curves (Hastie and Stuetzle, 1989, Sandilya and Kukarni, 2002); beamlets, curvelets, ridgelets . . . (Cand´ es 1999; Cand´ es and Donoho, 1999; Donoho and Huo, 2002). Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Statistical literature Above literature: No statistical quantifications. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Statistical literature Above literature: No statistical quantifications. Statistical literature: Cheng, Hall and Hartigan (2004); Arias-Castro, Donoho, and Huo (2006); Genovese et al. (2009, 2012, 2014); Chen et al. (2013, 2014) Qiao and WP (2015) Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Genovese et al. (2009): Path density • X x 0 ( t ) integral curve of gradient field; starting at x 0 V ( A ) = { x 0 : X x 0 ∩ A � = ∅} Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Genovese et al. (2009): Path density • X x 0 ( t ) integral curve of gradient field; starting at x 0 V ( A ) = { x 0 : X x 0 ∩ A � = ∅} (purple area) Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Genovese et al. (2009): Path density • X x 0 ( t ) integral curve of gradient field; starting at x 0 V ( A ) = { x 0 : X x 0 ∩ A � = ∅} (purple area) � • Path measure π ( A ) = V ( A ) g ( x ) dx Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Genovese et al. (2009): Path density • X x 0 ( t ) integral curve of gradient field; starting at x 0 V ( A ) = { x 0 : X x 0 ∩ A � = ∅} (purple area) � • Path measure π ( A ) = V ( A ) g ( x ) dx • Path density p : � = ∞ π ( B ( x , r )) for x on filament p ( x ) = lim = r < ∞ for x off filament r → 0 Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Genovese et al. (2009): Path density • X x 0 ( t ) integral curve of gradient field; starting at x 0 V ( A ) = { x 0 : X x 0 ∩ A � = ∅} (purple area) � • Path measure π ( A ) = V ( A ) g ( x ) dx • Path density p : � = ∞ π ( B ( x , r )) for x on filament p ( x ) = lim = r < ∞ for x off filament r → 0 • Consider level set of estimated path density as ‘estimator’. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Path density Galaxy distribution in a slice Data source: www.mpa-garching.mpg.de Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament A different model Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament A different model Filament: M = { f ( x ) : x ∈ [0 , 1] } ⊂ R d . Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament A different model Filament: M = { f ( x ) : x ∈ [0 , 1] } ⊂ R d . Genovese et al. (2012a) consider the model Y i = f ( U i ) + ǫ Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament A different model Filament: M = { f ( x ) : x ∈ [0 , 1] } ⊂ R d . Genovese et al. (2012a) consider the model Y i = f ( U i ) + ǫ with U i drawn from a distribution on [0 , 1] Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament A different model Filament: M = { f ( x ) : x ∈ [0 , 1] } ⊂ R d . Genovese et al. (2012a) consider the model Y i = f ( U i ) + ǫ with U i drawn from a distribution on [0 , 1] ǫ i independent such that support( Y ) = M ⊕ σ Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament A different model Filament: M = { f ( x ) : x ∈ [0 , 1] } ⊂ R d . Genovese et al. (2012a) consider the model Y i = f ( U i ) + ǫ with U i drawn from a distribution on [0 , 1] ǫ i independent such that support( Y ) = M ⊕ σ Minimax rates for estimating the filament f using Hausdorff distance are derived in this model. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament A different model Filament: M = { f ( x ) : x ∈ [0 , 1] } ⊂ R d . Genovese et al. (2012a) consider the model Y i = f ( U i ) + ǫ with U i drawn from a distribution on [0 , 1] ǫ i independent such that support( Y ) = M ⊕ σ Minimax rates for estimating the filament f using Hausdorff distance are derived in this model. Genovese et al. (2012b) consider the medial axis of the level set to estimate the filament. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Medial axis ● ● Fig 3 . The Medial Axis. Top left: a set S . Top right: a non-medial ball contained in S ; Bottom left: a medial ball that touches the boundary of S in 2 places. Bottom right: the medial axis consists of the centers of the medial balls. From Genovese et al. 2012. Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Distribution theory for filament estimation Qiao and WP (2015) d = 2 Nonparametric Inference for Geometric Objects
Overview Integral curves Level set estimation Inference for modes / modal clustering Filament Ridge estimation via bump hunting We now consider filament estimation based on iid observations from a density f assuming the existence of a ridge line. Recall Nonparametric Inference for Geometric Objects
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