Locality Sensitive Discriminative Unsupervised Dimensionality Reduction
نویسندگان
چکیده
منابع مشابه
Discriminative Unsupervised Dimensionality Reduction
As an important machine learning topic, dimensionality reduction has been widely studied and utilized in various kinds of areas. A multitude of dimensionality reduction methods have been developed, among which unsupervised dimensionality reduction is more desirable when obtaining label information requires onerous work. However, most previous unsupervised dimensionality reduction methods call f...
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Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we show that PCA and PCO can be carried out under regression frameworks. Thus, it is convenient to incorporate sparse techniques into the regression frameworks. In particular, we propose a sparse PCA model and a sparse PCO model. The for...
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Modern nonlinear dimensionality reduction (DR) techniques project high dimensional data to low dimensions for their visual inspection. Provided the intrinsic data dimensionality is larger than two, DR necessarily faces information loss and the problem becomes ill-posed. Discriminative dimensionality reduction (DiDi) offers one intuitive way to reduce this ambiguity: it allows a practitioner to ...
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ژورنال
عنوان ژورنال: Symmetry
سال: 2019
ISSN: 2073-8994
DOI: 10.3390/sym11081036