Inductive Hashing on Manifolds

TL;DR

This paper introduces an inductive hashing method that leverages manifold learning techniques, especially t-SNE, to generate compact binary codes that preserve intrinsic data structures, enabling scalable large-scale data retrieval.

Contribution

It presents an efficient, inductive approach to apply manifold learning for hashing, addressing out-of-sample data issues and enabling new flexible hashing techniques.

Findings

Effective binary embeddings on manifolds using t-SNE.

Addresses out-of-sample data problem in manifold-based hashing.

Enables scalable large-scale data retrieval with structure-preserving codes.

Abstract

Learning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes that preserve the Euclidean distance in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexity of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. We particularly show that hashing on the basis of t-SNE .