A Multiplicative Algorithm for Convolutive Non-Negative Matrix Factorization Based on Squared Euclidean Distance.pdf (409.53 kB)
A multiplicative algorithm for convolutive non-negative matrix factorization based on squared euclidean distance
journal contributionposted on 2009-12-22, 16:46 authored by Wenwu Wang, Andrzej Cichocki, Jonathon Chambers
Using the convolutive nonnegative matrix factorization (NMF) model due to Smaragdis, we develop a novel algorithm for matrix decomposition based on the squared Euclidean distance criterion. The algorithm features new formally derived learning rules and an efficient update for the reconstructed nonnegative matrix. Performance comparisons in terms of computational load and audio onset detection accuracy indicate the advantage of the Euclidean distance criterion over the Kullback–Leibler divergence criterion.
- Mechanical, Electrical and Manufacturing Engineering
CitationWANG, W, CICHOCKI, A and CHAMBERS, J., 2009. A multiplicative algorithm for convolutive non-negative matrix factorization based on squared euclidean distance. IEEE Transactions on Signal Processing, 57, (7), pp. 2858-2864.
- VoR (Version of Record)
NotesThis article was published in the journal IEEE Transactions on Signal Processing [© IEEE] and is also available at: http://ieeexplore.ieee.org/. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.