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- W161751758 abstract "Assume our data consists of unit vectors (directions) and we are to nd a small orthogonal set of the the most important directions summarizing the data. We develop online algorithms for this type of problem. The techniques used are similar to Principal Component Analysis which nds the most important small rank subspace of the data. The new problem is signicantly more complex since the online algorithm maintains uncertainty over the most relevant subspace as well as directional information." @default.
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- W161751758 date "2013-01-01" @default.
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- W161751758 title "Learning a set of directions" @default.
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