Joint Principal Component Analysis and Supervised k Means Algorithm via Non-Iterative Analytic Optimization Approach

It is worth noting that the traditional methods for performing both the dimensional reduction and the classification are via the two steps iterative approaches. In this case, performing the dimensional reduction does not consider the classification. On the other hand, the classification is performed...

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Vydané v:	IEEE transactions on signal processing Ročník 72; s. 1348 - 1360
Hlavní autori:	Zhang, Zhanbin, Ling, Bingo Wing-Kuen, Huang, Guoheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Arithmetic Classification Classification algorithms Clusters EEG Exact solutions Joint PCA and supervised k means algorithm Mathematical analysis Matrices (mathematics) mental arithmetic classification non-iterative analytic optimization Optimization Principal component analysis Principal components analysis Reduction Signal processing algorithms singular value decomposition Symmetric matrices symmetric matrix Transforms
ISSN:	1053-587X, 1941-0476
On-line prístup:	Získať plný text
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