Exploring Sparse Subspace Clustering: Theory and Practice

I looked into sparse subspace clustering (SSC). SSC was introduced by Elhamifar and Vidal in their paper “Sparse Subspace Clustering: Algorithm, Theory, and Applications”. It was revisited by Matsushima and Brbic in their paper “Selective Sampling-based Scalable Sparse Subspace Clustering”. In this video, the focus is on (1) how to find a sparse solution for subspace clustering (2) the difference between independent and disjoint subspaces (3) coding SSC in python and testing it on a couple of synthetic datasets. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🔗 notes+code: https://mashaan14.github.io/YouTube-channel/spectral_clustering/2024_02_13_SSC ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - website https://mashaan14.github.io/mashaan/ - github https://github.com/mashaan14 - X https://twitter.com/mashaan_14 - linkedin https://linkedin.com/in/mashaan ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Chapters: 0:00 start 0:13 reading the paper 0:52 problem statement 1:39 an example of a subspace 2:12 self-expressiveness property 3:32 visualizing sparse-subspace 5:46 notes on representing a point 6:26 independent and disjoint subspaces 7:55 sparse subspace clustering (SSC) 8:12 code for SSC 8:40 creating synthetic datasets 10:03 a function for finding a sparse solution 11:43 a function for SSC adjacency 12:52 my notes on numpy argpartition 14:13 visualizing the adjacency matrix 15:13 the results of clustering 16:13 clustering iris dataset 17:11 final remarks ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #graph #spectralclustering #subspace #subspaceclustering #sparsesubspaceclustering #PCA #DeepLearningTutorial #MachineLearningProject #AIResearch #CodingTutorial