A Multiplicative Algorithm for Convolutive Non-Negative Matrix Factorization Based on Squared Euclidean Distance.pdf (409.53 kB)
Download fileA multiplicative algorithm for convolutive non-negative matrix factorization based on squared euclidean distance
journal contribution
posted on 2009-12-22, 16:46 authored by Wenwu Wang, Andrzej Cichocki, Jonathon ChambersUsing 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.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Citation
WANG, 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.Publisher
© IEEEVersion
- VoR (Version of Record)
Publication date
2009Notes
This 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.ISSN
1053-587XLanguage
- en