Rank Subspace Learning for Compact Hash Codes

TL;DR

This paper introduces a novel hash learning framework that encodes feature rank orders in low-dimensional subspaces, improving efficiency and stability for large-scale nearest neighbor search.

Contribution

It proposes a new ranking-based hashing method that generalizes WTA hash, optimizing rank correlations for high-precision, short code length hashing.

Findings

Outperforms state-of-the-art hashing algorithms in various datasets.

Provides a stable and efficient ranking-based hashing framework.

Offers two optimization strategies: independent and sequential learning.

Abstract

The era of Big Data has spawned unprecedented interests in developing hashing algorithms for efficient storage and fast nearest neighbor search. Most existing work learn hash functions that are numeric quantizations of feature values in projected feature space. In this work, we propose a novel hash learning framework that encodes feature's rank orders instead of numeric values in a number of optimal low-dimensional ranking subspaces. We formulate the ranking subspace learning problem as the optimization of a piece-wise linear convex-concave function and present two versions of our algorithm: one with independent optimization of each hash bit and the other exploiting a sequential learning framework. Our work is a generalization of the Winner-Take-All (WTA) hash family and naturally enjoys all the numeric stability benefits of rank correlation measures while being optimized to achieve high precision at very short code length. We compare with several state-of-the-art hashing algorithms in both supervised and unsupervised domain, showing superior performance in a number of data sets.