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Poster
On Algorithms for Sparse Multi-factor NMF
Siwei Lyu · Xin Wang

Sun Dec 08 02:00 PM -- 06:00 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

Nonnegative matrix factorization (NMF) is a popular data analysis method, the objective of which is to decompose a matrix with all nonnegative components into the product of two other nonnegative matrices. In this work, we describe a new simple and efficient algorithm for multi-factor nonnegative matrix factorization problem ({mfNMF}), which generalizes the original NMF problem to more than two factors. Furthermore, we extend the mfNMF algorithm to incorporate a regularizer based on Dirichlet distribution over normalized columns to encourage sparsity in the obtained factors. Our sparse NMF algorithm affords a closed form and an intuitive interpretation, and is more efficient in comparison with previous works that use fix point iterations. We demonstrate the effectiveness and efficiency of our algorithms on both synthetic and real data sets.

Author Information

Siwei Lyu (SUNY at Albany)
Xin Wang (SUNY at Albany)

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