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Anomaly Detection and Prototype Selection Using Polyhedron Curvature Benyamin Ghojogh, Fakhri Karray, Mark Crowley Canadian AI conference, 2020 1 Anomaly Detection finding outliers or anomalies which differ significantly from the normal


  1. Anomaly Detection and Prototype Selection Using Polyhedron Curvature Benyamin Ghojogh, Fakhri Karray, Mark Crowley Canadian AI conference, 2020 1

  2. Anomaly Detection • finding outliers or anomalies which differ significantly from the normal data points • fraud detection, intrusion detection, medical diagnosis, and damage detection • Some methods: • Local Outlier Factor (LOF) • One-class SVM • Elliptic Envelope (EE) • Isolation forest 2

  3. Prototype Selection • also referred to as instance ranking and numerosity reduction • Two versions: • Ranking based • Retaining based • Some methods: • Edited Nearest Neighbor (ENN) • Decremental Reduction Optimization Procedure 3 (DROP3) • Stratified Ordered Selection (SOS) • Shell Extraction (SE) • Principal Sample Analysis (PSA) • Instance Ranking by Matrix Decomposition (IRMD) 3

  4. Polyhedron Curvature • Polytope: a geometrical object in R^d whose faces are planar • Special cases: • Polygon: polytope in R^2 • Polyhedron: polytope in R^3 • Consider a polygon where τj and µj are the interior and exterior angles at the j-th vertex • we have τj +µj = π 4

  5. Polyhedron Curvature • Thomas Harriot’s theorem proposed in 1603: • if this geodesic on the unit sphere is a triangle, its area is µ1 +µ2 +µ3 −π = 2π −(τ1 +τ2 +τ3) • generalization of this theorem from a geodesic triangular polygon (3-gon) to an k-gon 𝑙 is µ1 + · · · + µk − kπ + 2π = 2π − σ 𝑏=1 τa 𝑙 • Descartes’s angular defect: 2π − σ 𝑏=1 τa 5

  6. Polyhedron Curvature 𝑙 • Descartes’s angular defect: D(x) = 2π − σ 𝑏=1 τa • total defect of a polyhedron with v vertices, e edges, and f faces is: 𝑙 D := σ 𝑏=1 D(xi) = 2π(v − e + f). • T erm v − e + f is Euler -Poincare characteristic of the polyhedron • The smaller τ angles result in sharper corner of the polyhedron • So, we can consider the angular defect as the curvature of the vertex 6

  7. Curvature Anomaly Detection (CAD) • Every data point is considered to be the vertex of a hypothetical polyhedron • For every point, we find its k-Nearest Neighbors (k-NN) • The k neighbors of the point (vertex) form the k faces of a polyhedron meeting at that vertex. • The more curvature that point (vertex) has, the more anomalous it is, i.e., far away (different) from its neighbors • So, anomaly score s_A is proportional to the curvature. 7

  8. Curvature Anomaly Detection (CAD) 𝑙 • Descartes’s angular defect: 2π − σ 𝑏=1 . Hence, curvature is τa proportional to minus the summation of angles • S_A(xi) ∝ 1/ τ a ∝ cos( τ a) 𝑙 • S_A(xi) := σ 𝑏=1 cos(τa) = 𝑙 σ 𝑏=1 (x’_a x’_a+1) / (||x’_a||_2 ||x’_a+1||2) • x’_a := x_a − x_i • Relaxation: Relaxation is valid 8

  9. Curvature Anomaly Detection (CAD) • Finding anomalies (training data): • Scree plot • K-means with two clusters: Cluster with larger mean is anomaly • Finding anomalies (out-of-sample): • k-NN for the out-of-sample point where the neighbors are from the training points • Calculate anomaly score • Compare with the means of clusters 9

  10. Kernel Curvature Anomaly Detection (K-CAD) • Pattern of normal and anomalous data might not be linear. • Done in feature space: (1) finding k-NN, (2) calculating the anomaly score • Kernel: k(x1, x2) := φ(x1)^T φ(x2) • Euclidean distance in the feature space: • Normalize the kernel: 10

  11. Kernel Curvature Anomaly Detection (K-CAD) • Score: • anomaly score in K-CAD is ranked inversely for some kernels such as Radial Basis Function (RBF), Laplacian, and polynomial (different degrees) • Reason: future work • M ultiply the scores by −1 or take the K -means cluster with smaller mean as the anomaly cluster 11

  12. Anomaly Landscape • anomaly landscape: the landscape in the input space whose value at every point in the space is the anomaly score computed by CAD or K- CAD. • two types of anomaly landscape: • all the training data points are used for k-NN • or merely the non-anomaly training points are used for k-NN 12

  13. Anomaly Paths • anomaly path: the path that an anomalous point has traversed from its not-known-yet normal version to become anomalous. Conversely, it is the path that an anomalous point should traverse to become normal • anomaly path can be used to make a normal sample anomalous or vice-versa 13

  14. Inverse Curvature Anomaly Detection (iCAD) • Score: • Two versions: • Rank based: ranking the points with the ranking score • Retaining based: apply K-means clustering, with two clusters, to the ranking scores and take the points of the cluster with larger mean 14

  15. Kernel Inverse Curvature Anomaly Detection (K-iCAD) • Scores: • iCAD and K-iCAD are task agnostic: • Classification: apply the method for every class • Regression and clustering: the method is applied on the entire data. 15

  16. Experiments: anomaly landscape 16

  17. Experiments: anomaly paths 17

  18. An application in image denoising 18

  19. Experiments: anomaly detection In most cases, K-CAD has better performance than CAD In many cases, we are better than the baseline methods We are also very fast 19

  20. Experiments: Effect of k Almost robust to change of k 20

  21. Experiments: prototype selection on synthetic data 21

  22. Prototype selection Outperform many of the baseline methods: • in both accuracy and time • In both ranking and retaining based approaches 22

  23. Future Direction • Try the idea of curvature for manifold embedding to propose a curvature preserving embedding method. 23

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