2020
DOI: 10.1109/access.2020.3037956
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Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval

Abstract: Traditional non-negative matrix factorization methods cannot learn the subspace from the high-dimensional data space composed of binary codes. One hopes to discover a compact parts-based representation composed of binary codes, which can uncover the intrinsic information and simultaneously respect the geometric structure of the original data. For this purpose, we introduce discrete hashing methods and propose a novel non-negative matrix factorization to generate binary codes from the original data. In this pap… Show more

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