Presentation slides for "Confident Kernel Sparse Coding and Dictionary Learning" paper at ICDM 2018 conference.

1. Confident Kernel Sparse Coding
and Dictionary Learning
Babak Hosseini
Prof. Dr. Barbara Hammer
Singapore, 20 Nov. 2018
bhosseini@techfak.uni-bielefeld.de
Cognitive Interaction Technology Centre of Excellence (CITEC)
Bielefeld University, Germany

2. Outlines
• Introduction
• Confident Dictionary Learning
• Experiments
• Conclusion
2

3. Introduction
• Introduction
• Confident Dictionary Learning
• Experiments
• Conclusion
3

4. Introduction
• Dictionary learning and sparse coding
• X: Input signals
• U: Dictionary matrix
• Γ: Sparse codes
• Reconstructing X using sparse resources from U
4

5. Introduction
• Dictionary learning and sparse coding
• X: Input signals
• U: Dictionary matrix
• Γ: Sparse codes
• Reconstructing X using sparse resources from U
5
x U γ

6. Introduction
• Dictionary learning and sparse coding
• X: Input signals
• U: Dictionary matrix
• Γ: Sparse codes
• Estimating Γ Sparse coding
• Estimating U Dictionary learning
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7. Introduction
• Kernel Dictionary learning/sparse coding
• Non-vectorial data
• Time-series
• Representation: Kernel matrix
• Pairwise-similarity/distance (x1,x2)
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8. Introduction
• Kernel Dictionary learning/sparse coding
• Implicit mapping
• Dictionary:
• Linear combination of training data
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9. Introduction
• Discriminative dictionary learning (DDL)
• Classification setting
• Label matrix
• Goal:
• Learn a Dictionary U that reconstructs X via Γ
• Mapping Γ: X L
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10. Introduction
• Discriminative dictionary learning (DDL)
• Goal:
• Learn a Dictionary U that reconstructs X via Γ
• Mapping Γ: X L
• : Ensures the discriminative mapping for training data
• : Has access to label information L
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11. Introduction
• What is the problem?
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12. Introduction
• What is the problem?
• The test and train models are not consistent!
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13. Introduction
• What is the problem?
• The test and train models are not consistent!
• Reconstruction of test data
doesn’t follow the discriminative mapping.
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!

14. Outlines
• Introduction
• Confident Dictionary Learning
• Experiments
• Conclusion
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15. Confident Dictionary Learning
• A new discriminant objective
•
• What is the point of that?!
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16. Confident Dictionary Learning
• A new discriminant objective
•
• Reconstruction model:
• :
• Its entries show the share of each class in the reconstruction of x.
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17. Confident Dictionary Learning
• A new discriminant objective
•
• Reconstruction model:
Therefore,
• Mapping to label space:
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18. Confident Dictionary Learning
• A new discriminant objective
•
• Minimizing
each x is reconstructed mostly by its own class.
• Flexible term: x still can use other classes (minor share)
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19. Confident Dictionary Learning
• Consistency?
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20. Confident Dictionary Learning
• Classification of test data:
• z: test data
• Label :
• Class j: highest contribution in the reconstruction
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21. • Classification of test data:
• z: test data
• Class j: highest contribution in the reconstruction
•
• Minimizing
Forcing to reconstruct z using less number of classes.
• Flexible: can still use small share of other classes (if required).
• Confident toward one class.
Confident Dictionary Learning
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22. Confident Dictionary Learning
• Convexity?
• Not convex!
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23. Confident Dictionary Learning
• Convexity?
• Not convex!
• β: -{most negative eigenvalue of V}
• Once before the training!
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24. Confident Dictionary Learning
• Consistent?
• Recall uses L too.
• Recall objective term
similar to the train’s
• Flexible contributions
• Confidence criteria
• More consistent
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25. Outlines
• Introduction
• Confident Dictionary Learning
• Experiments
• Conclusion
25

26. Experiments
• Datasets:
• Multi-dimensional time-series
• Cricket Umpire [1]:
• Articulatory Words [2]
• Schunk Dexterous [3]:
• UTKinect Actions [4]:
• DynTex++[5]:
[1] M. H. Ko, et al. G. W. West, S. Venkatesh, and M. Kumar, “Online context recognition in multisensor systems using dynamic
time warping,” in ISSNIP’05. IEEE, 2005, pp. 283–288.
[2] J. Wang, A. Samal, and J. Green, “Preliminary test of a real-time, interactive silent speech interface based on electromagnetic
articulograph,” in SLPAT’14, 2014, pp. 38–45.
[3] A. Drimus, G. Kootstra, A. Bilberg, and D. Kragic, “Design of a flexible tactile sensor for classification of rigid and deformable
objects,” Robotics and Autonomous Systems, vol. 62, no. 1, pp. 3–15, 2014.
[4] M. Madry, L. Bo, D. Kragic, and D. Fox, “St-hmp: Unsupervised spatiotemporal feature learning for tactile data,” in ICRA’14.
IEEE, 2014, pp. 2262–2269.
[5] L. Xia, C.-C. Chen, and J. Aggarwal, “View invariant human action recognition using histograms of 3d joints,” in CVPRW’12
Workshops. IEEE, 2012, pp. 20–27.
[6] B. Ghanem and N. Ahuja, “Maximum margin distance learning for dynamic texture recognition,” in ECCV’10. Springer, 2010,
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27. Experiments
• Baselines:
• K-KSVD: Kernel K-SVD
• LC-NNKSC: Predecessor of CKSC
• EKDL: Equiangular kernel dictionary learning
• KGDL: Dictionary learning on grassmann manifolds
• LP-KSVD: Locality preserving K-SVD
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28. Experiments
• Baselines:
• K-KSVD: Kernel K-SVD
• LC-NNKSC: Predecessor of CKSC
• EKDL: Equiangular kernel dictionary learning
• KGDL: Dictionary learning on grassmann manifolds
• LP-KSVD: Locality preserving K-SVD
• Classification Accuracy (%):
• “Dictionary discrimination power”
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29. Experiments
• Interpretability of atoms (IP):
• Dictionary:
•
• In range [1/c 1]
• atom i
• 1 if atom i belongs only to one class
• 1/c if atom i is related to all classes
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30. Experiments
• Interpretability of atoms (IP):
•
• In range [1/c 1]
• 1 if atom i belongs only to one class
• 1/c if atom i is related to all classes
• Good discrimination
• Good interpretation
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31. Conclusion
• Consistency is important for DDL models.
• Proposed a flexible discriminant terms for DDL.
• Proposed a more consistent training-recall framework.
• Increase in:
• Discriminative performance
• Interpretability of dictionary atoms.
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32. Thank you very much!
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