Face Recognition Based on Probabilistic Subspace Clustering Via Sparse Representations
Bing Peng · Video Engineering · 2014
To implement rapidly clustering for the very large signal collections problem in subspace clustering applications,a probabilistic subspace clustering algorithm based on sparse representations is proposed. Firstly,each signal is represented by a sparse combination of basis elements( atoms),which form the columns of a dictionary matrix. Then,the set of sparse representations is utilized to derive the co-occurrences matrix of atoms and signals,which is modeled as emerging from a mixture model. Finally,the components of the mixture model are obtained via a non-negative matrix factorization( NNMF) of the co-occurrences matrix,and the subspace of each signal is estimated according to a maximum-likelihood( ML) criterion. Experimental results on YaleB face database show that proposed method has better clustering accuracies comparing with several latest approaches.