Explore subspace clustering techniques in this 34-minute lecture by Laura Balzano from the University of Michigan. Delve into the concept of ensembles of K-Subspaces, examining its applications, results, and intuition. Learn about random initializations, one-dimensional problems, and angular separation. Compare clustering results with other algorithms such as SSC (Sparse Subspace Clustering). Gain insights into the latest developments in randomized numerical linear algebra and its applications in subspace clustering.
Subspace Clustering Using Ensembles of K-Subspaces
Overview
Syllabus
Introduction
Acknowledgements
Applications
Results
What we know
Intuition
Overview
Random initializations
Onedimensional problems
Angular separation
Clustering Results
Other Algorithms
SSC
SSC Results
Summary
Conclusion
Taught by
Simons Institute
