The Influence Function of Principal Component Analysis by Self-Organizing Rule

Autor:	Shinto Eguchi, Isao Higuchi
Rok vydání:	1998
Předmět:	Artificial neural network Covariance matrix business.industry Cognitive Neuroscience Sparse PCA Pattern recognition Arts and Humanities (miscellaneous) Robustness (computer science) Outlier Principal component analysis Learning rule Artificial intelligence business Vector calculus Mathematics
Zdroj:	Neural computation. 10(6)
ISSN:	1530-888X
Popis:	This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcd1b24547054cded93e0d05eb47cb7b https://pubmed.ncbi.nlm.nih.gov/9698351 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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