CUR matrix decompositions for improved data analysis
نویسندگان
چکیده
منابع مشابه
CUR matrix decompositions for improved data analysis.
Principal components analysis and, more generally, the Singular Value Decomposition are fundamental data analysis tools that express a data matrix in terms of a sequence of orthogonal or uncorrelated vectors of decreasing importance. Unfortunately, being linear combinations of up to all the data points, these vectors are notoriously difficult to interpret in terms of the data and processes gene...
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Many data analysis applications deal with large matrices and involve approximating the matrix using a small number of “components.” Typically, these components are linear combinations of the rows and columns of the matrix, and are thus difficult to interpret in terms of the original features of the input data. In this paper, we propose and study matrix approximations that are explicitly express...
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The manuscript describes efficient algorithms for the computation of the CUR and ID decompositions. The methods used are based on simple modifications to the classical truncated pivoted QR decomposition, which means that highly optimized library codes can be utilized for implementation. For certain applications, further acceleration can be attained by incorporating techniques based on randomize...
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ژورنال
عنوان ژورنال: Proceedings of the National Academy of Sciences
سال: 2009
ISSN: 0027-8424,1091-6490
DOI: 10.1073/pnas.0803205106