Randomized projective methods for the construction of binary sparse vector representations

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 similarity-difference between initial vectors composed of floating-point 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.