We consider the problem of clustering data lying on multiple subspaces of unknown and possibly different dimensions. We show that one can represent the subspaces with a set of pol...
: Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerate...
In [1], three popular subspace face recognition methods, PCA, Bayes, and LDA were analyzed under the same framework and an unified subspace analysis was proposed. However, since t...
We show a variety of ways to cluster student activity datasets using different clustering and subspace clustering algorithms. Our results suggest that each algorithm has its own st...
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...