O. Okun (Finland) and H. Priisalu (Estonia)
pattern recognition, nonnegative matrix factorization
In this paper, linear and unsupervised dimensionality re duction via matrix factorization with nonnegativity con straints is studied when applied for feature extraction, fol lowed by pattern recognition. Since typically matrix fac torization is iteratively done, convergence can be slow. To alleviate this problem, a significantly (more than 11 times) faster algorithm is proposed, which does not cause severe degradations in classification accuracy when dimensional ity reduction is followed by classification. Such results are due to two modifications of the previous algorithms: fea ture scaling (normalization) prior to the beginning of iter ations and initialization of iterations, combining two tech niques for mapping unseen data.
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