SIAM MDS20 MS13 Advances in Subspace Learning and Clustering Mini-Symposium

Data that comes from a union of subspaces is prevalent in important applications such as face clustering, motion segmentation, and multi-view geometry in computer vision.  Although many powerful approaches have been designed to handle such data, challenges still remain such as handling noise and missing entries in the data, imbalance of data across classes, heterogeneity in the data, and scalable algorithms to handle modern large scale datasets, to name a few.  This mini-symposium will present recent advances in subspace learning and clustering which use tools from algebraic geometry, statistics, sparse and low-rank representation, and machine learning to address the aforementioned challenges.


Daniel Robinson
Lehigh University US

Rene Vidal

Rene Vidal
Johns Hopkins University

To view all the mini-symposium presentations please watch the following video


Yi Ma, University of California, Berkeley, U.S.

Madeleine Udell, Cornell University, U.S

Richard J. Kueng, California Institute of Technology, U.S

Chong You, University of California, Berkeley, U.S.

Daniel Pimentel-Alarcón, University of Wisconsin, Madison, U.S.

Mahdi Soltanolkotabi, University of Southern California, U.S

John Lipor, Portland State University, U.S


TimeSpeakerAffiliationTitle of Presentation

Monday June 22

11:00 Yi MaUniversity of California, BerkeleyComplete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group
11:30 Madeleine UdellCornell UniversityImputing Missing Data with the Gaussian Copula
12:00 Richard J Kueng & Joel A TroppCalifornia Institute of TechnologyBinary Component Decomposition
12:30 Chong YouUniversity of California, BerkeleyScalable Elastic-Net Subspace Clustering
1:00 Daniel Pimentel-AlarconUniversity of Wisconsin, MadisonFusion Subspace Clustering for Missing Data
1:30 Mahdi SoltanolkotabiUniversity of Southern CaliforniaDenoising via Early Stopping: Towards Demystifying Deep Image Priors
2:00 John LiporPortland State UniversityClustering Quality Metrics for Subspace Clustering