期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 81, 期 1, 页码 94-106出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2005.10.003
关键词
non-negative matrix factorization; Fisher's linear discriminant analysis; classification; olive oil; EEMs
Non-negative matrix factorization (NMF) is a technique that decomposes multivariate data into a smaller number of basis functions and encodings using non-negative constraints. These constraints make that only positive solutions can be obtained and thus this method provides a more realistic approximation to the original data than other factorization methods that allow positive and negative values. Here we show that NMF is a powerful technique for learning a meaningful parts-based representation of the fluorescence excitation-emission matrices (EEMs) of different sets of olive oils. The capabilities of NW used together with Fisher's LDA for discriminating between various types of oils were also studied. In all cases, good classifications were obtained (90-100%). The classification results obtained with the proposed method were compared to those obtained using two other classification methods (parallel factor analysis (PARAFAC) combined with Fisher's LDA and discriminant multi-way partial least squares regression (DN-PLSR)). (c) 2005 Elsevier B.V. All rights reserved.
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