Revisiting Nearest Neighbors from a Sparse Signal Approximation View

Revisiting Nearest Neighbors from a Sparse Signal Approximation View

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Revisiting Nearest Neighbors from a Sparse Signal Approximation View

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  1. 1 Intro
  2. 2 Revisiting Nearest Neighbors from a Sparse Signal approximation view
  3. 3 What is a neighborhood?
  4. 4 Neighborhood definitions: Kernels (Similarity)
  5. 5 Neighborhood definitions: Local linearity
  6. 6 Interlude: Sparse Signal Approximation
  7. 7 Neighborhood = Sparse signal approximation
  8. 8 Alternative: Basis pursuit
  9. 9 Neighborhoods: Non-negative basis pursuit
  10. 10 Non-Negative Kernel regression (NNK)
  11. 11 Geometry: Kernel Ratio Interval (KRI)
  12. 12 Example
  13. 13 Label propagation using graphs
  14. 14 Experiments: Label propagation
  15. 15 Experiments: Classification
  16. 16 Neighborhoods Summary
  17. 17 Conventional Approach
  18. 18 Solution: NNK-Means
  19. 19 kMeans vs Dictionary learning
  20. 20 Case study: Detecting unseen data using NNK-Means (representational outliers)
  21. 21 NNK-Means atom use in each scenario
  22. 22 NNK-Means for Outlier Detection
  23. 23 NNK-Means Summary
  24. 24 What is deep learning?
  25. 25 Graph based view of deep learning
  26. 26 NNK interpolation at penultimate layer

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