Procrustean Orthogonal Sparse Hashing
This addresses the need for efficient similarity search in machine learning, offering a biologically-inspired approach with empirical gains, though it appears incremental as it builds on existing olfaction-inspired methods.
The paper tackled the problem of improving similarity search via sparse hashing by proving that a biological mechanism from insect olfaction solves an optimization problem and showing orthogonality boosts accuracy, resulting in a novel method (POSH) that outperforms state-of-the-art hashing methods across benchmarks.
Hashing is one of the most popular methods for similarity search because of its speed and efficiency. Dense binary hashing is prevalent in the literature. Recently, insect olfaction was shown to be structurally and functionally analogous to sparse hashing [6]. Here, we prove that this biological mechanism is the solution to a well-posed optimization problem. Furthermore, we show that orthogonality increases the accuracy of sparse hashing. Next, we present a novel method, Procrustean Orthogonal Sparse Hashing (POSH), that unifies these findings, learning an orthogonal transform from training data compatible with the sparse hashing mechanism. We provide theoretical evidence of the shortcomings of Optimal Sparse Lifting (OSL) [22] and BioHash [30], two related olfaction-inspired methods, and propose two new methods, Binary OSL and SphericalHash, to address these deficiencies. We compare POSH, Binary OSL, and SphericalHash to several state-of-the-art hashing methods and provide empirical results for the superiority of the proposed methods across a wide range of standard benchmarks and parameter settings.