Analysis of unsupervised dimensionality reduction techniques
نویسندگان
چکیده
منابع مشابه
Analysis of unsupervised dimensionality reduction techniques
Domains such as text, images etc contain large amounts of redundancies and ambiguities among the attributes which result in considerable noise effects (i.e. the data is high dimension). Retrieving the data from high dimensional datasets is a big challenge. Dimensionality reduction techniques have been a successful avenue for automatically extracting the latent concepts by removing the noise and...
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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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How can we represent a data residing in high dimensional space onto a low dimensional space without the loss of important information? In image processing, pattern recognition, machine learning and in many other fields like social science, statistics, signal processing etc, the measured data set often resides in a very high dimensional space which leads to a number of computational and represen...
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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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Text documents are complex high dimensional objects. To effectively visualize such data it is important to reduce its dimensionality and visualize the low dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore dimensionality reduction methods that draw upon domain knowledge in order to achieve a better low dimensional embedding and visualization of documents. We consider t...
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ژورنال
عنوان ژورنال: Computer Science and Information Systems
سال: 2009
ISSN: 1820-0214,2406-1018
DOI: 10.2298/csis0902217k