Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
نویسندگان
چکیده
منابع مشابه
Dimension Selection for Feature Selection and Dimension Reduction with Principal and Independent Component Analysis
This letter is concerned with the problem of selecting the best or most informative dimension for dimension reduction and feature extraction in high-dimensional data. The dimension of the data is reduced by principal component analysis; subsequent application of independent component analysis to the principal component scores determines the most nongaussian directions in the lower-dimensional s...
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ژورنال
عنوان ژورنال: Engineering Applications of Artificial Intelligence
سال: 2017
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2017.07.004