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• W2073605511 abstract "Confronted with high-dimensional data arising from either word-document count, global climate patterns or any one of the myriad other sources, most scientific approaches extract a good low-dimensional summary. This desire to reduce dimensionality may be seen as a consequence of Occam's Razor, and the scientific methodologies we have in mind include those from data mining and statistics." @default.
• W2073605511 created "2016-06-24" @default.
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• W2073605511 date "2007-01-07" @default.
• W2073605511 modified "2023-09-26" @default.
• W2073605511 title "Efficient subspace approximation algorithms" @default.
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• W2073605511 doi "https://doi.org/10.5555/1283383.1283440" @default.
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