Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information
نویسندگان
چکیده
منابع مشابه
Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information
The goal of dimension reduction is to represent high-dimensional data in a lowerdimensional subspace, while intrinsic properties of the original data are kept as much as possible. An important challenge in unsupervised dimension reduction is the choice of tuning parameters, because no supervised information is available and thus parameter selection tends to be subjective and heuristic. In this ...
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ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2014
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2014edl8111