Revisiting Locality-Sensitive Binary Codes from Random Fourier Features

Xiaoyun Li, Ping Li

Abstract: The method of Random Fourier Feature (RFF) has been popular for large-scale learning, which generates non-linear random features of the data. It has also been used to construct binary codes via stochastic quantization for efficient information retrieval. In this paper, we revisit binary hashing from RFF, and propose SignRFF, a new and simple strategy to extract RFF-based binary codes. We show the locality-sensitivity of SignRFF, and propose a new measure, called ranking efficiency, to theoretically compare different Locality-Sensitive Hashing (LSH) methods with practical implications. Experiments are conducted to show that the proposed SignRFF is consistently better than the previous RFF-based method, and also outperforms other data-dependent and deep learning based hashing methods with sufficient number of hash bits. Moreover, we also validate that the proposed ranking efficiency aligns well with the empirical search performance.