Sparse Subspace Clustering (SSC)



We 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.
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🔗 jupyter notebook I built in this video: https://github.com/mashaan14/YouTube-channel/blob/main/notebooks/2024_02_13_SSC.ipynb
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Spectral clustering tutorial: https://youtu.be/k7M1TMYac-Y
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– my website ➡️ https://mashaan14.github.io/mashaan/
– my github ➡️ https://github.com/mashaan14
– my linkedin ➡️ https://linkedin.com/in/mashaan
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📹 Video edit: Adobe Premiere Rush
🎧 Audio enhancement: Adobe Podcast
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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 representation
5:46 my notes on representing a point using other points from the subspace
6:26 independent and disjoint subspaces
7:55 sparse subspace clustering (SSC) algorithm
8:12 code for sparse subspace clustering (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
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#graph #spectralclustering #subspace #subspaceclustering #sparsesubspaceclustering #SSC #machinelearning #python #numpy #ai #artificialintelligence #PCA

Duration: 00:18:06

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