Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization

TL;DR

This paper introduces a scalable supervised hashing framework that efficiently learns binary codes for large datasets by decomposing the learning process and employing asymmetric matrix factorization, compatible with various hash functions.

Contribution

It proposes a novel scalable discrete supervised hash learning method using asymmetric matrix factorization and a new optimization algorithm, reducing complexity to O(n).

Findings

Outperforms or matches state-of-the-art hashing methods on large datasets.

Reduces computational complexity to linear time and space.

Compatible with deep neural networks and kernel methods.

Abstract

Hashing method maps similar data to binary hashcodes with smaller hamming distance, and it has received a broad attention due to its low storage cost and fast retrieval speed. However, the existing limitations make the present algorithms difficult to deal with large-scale datasets: (1) discrete constraints are involved in the learning of the hash function; (2) pairwise or triplet similarity is adopted to generate efficient hashcodes, resulting both time and space complexity are greater than O(n^2). To address these issues, we propose a novel discrete supervised hash learning framework which can be scalable to large-scale datasets. First, the discrete learning procedure is decomposed into a binary classifier learning scheme and binary codes learning scheme, which makes the learning procedure more efficient. Second, we adopt the Asymmetric Low-rank Matrix Factorization and propose the Fast Clustering-based Batch Coordinate Descent method, such that the time and space complexity is reduced to O(n). The proposed framework also provides a flexible paradigm to incorporate with arbitrary hash function, including deep neural networks and kernel methods. Experiments on large-scale datasets demonstrate that the proposed method is superior or comparable with state-of-the-art hashing algorithms.