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.
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