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Probabilistic Subspace Clustering Via Sparse Representations
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DOI：10.1109/LSP.2012.2229705.png)
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Abstract
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We present a probabilistic subspace clustering approach that is capable of rapidly clustering very large signal collections. Each signal is represented by a sparse combination of basis elements (atoms), which form the columns of a dictionary matrix. 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. 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. Performance evaluation demonstrate comparable clustering accuracies to state-of-the-art at a fraction of the computational load.
Keywords:
Aspect model
dictionary
non-negative matrix factorization
sparse representation
subspace clustering
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