Authors: D A Rachkovskij I S Misuno S V Slipchenko
Publish Date: 2012/02/04
Volume: 48, Issue: 1, Pages: 146-156
Abstract
Properties of randomized binary vector representations with adjustable sparseness are investigated Such representations are formed from input vectors by projecting them using a random matrix with ternary elements 1 0 +1 The accuracy of estimating measures of similaritydifference between initial vectors composed of floatingpoint numbers and output binary vectors is analyzed The vector representations obtained can be used to efficiently process large arrays of input multidimensional vectors in applications related to searching classification associative memory etc
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