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Kernel k-means and nonlinear embedding
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Classroom Contents
Clustering and Classification From the Core to the Edge - Thomas Strohmer, California University
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- 1 Intro
- 2 Outline
- 3 Unsupervised Learning via diffusion maps
- 4 Spectral clustering: learning the shape of data
- 5 Spectral clustering, graph cuts, community detection
- 6 Data clustering and unsupervised learning
- 7 Limitation of k-means
- 8 Kernel k-means and nonlinear embedding
- 9 Laplacian eigenmaps, k-means, spectral clustering
- 10 Comments on spectral clustering
- 11 A graph cut perspective
- 12 RatioCut and the graph Laplacian
- 13 Convex relaxation of RatioCut
- 14 Intuition
- 15 Finding the optimal graph cut via SDP relaxation
- 16 A short tour of the proof - Game of Cones
- 17 Spectral clustering for two concentric circles
- 18 Community detection under stochastic block model
- 19 Graph cuts and the stochastic block model
- 20 Semisupervised clustering
- 21 The Age of Surveillance Capitalism
- 22 What happens on the edge, stays on the edge!
- 23 Al on the edge
- 24 Challenges of on-device machine learning
- 25 Compressive machine learning
- 26 Compressive classification
- 27 Compressive deep learning
- 28 Construction of projection matrix
- 29 Structured manifold projection
- 30 Initial results on MNIST dataset
- 31 Conclusion and Outlook
