Structured Learning of Binary Codes with Column Generation

TL;DR

This paper introduces a novel column generation framework for learning binary hash functions that directly optimize multivariate performance measures like NDCG and AUC, improving large-scale information retrieval tasks.

Contribution

It presents a structured learning approach for hash function learning using column generation, enabling direct optimization of ranking-based performance measures.

Findings

Outperforms state-of-the-art hashing methods in ranking and retrieval tasks.

Effectively optimizes multivariate performance measures such as NDCG and AUC.

Demonstrates scalability and efficiency through stage-wise training and simplified loss functions.

Abstract

Hashing methods aim to learn a set of hash functions which map the original features to compact binary codes with similarity preserving in the Hamming space. Hashing has proven a valuable tool for large-scale information retrieval. We propose a column generation based binary code learning framework for data-dependent hash function learning. Given a set of triplets that encode the pairwise similarity comparison information, our column generation based method learns hash functions that preserve the relative comparison relations within the large-margin learning framework. Our method iteratively learns the best hash functions during the column generation procedure. Existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest---multivariate performance measures such as the AUC and NDCG. Our column generation based method can be further generalized from the triplet loss to a general structured learning based framework that allows one to directly optimize multivariate performance measures. For optimizing general ranking measures, the resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. We use a combination of column generation and cutting-plane techniques to solve the optimization problem. To speed-up the training we further explore stage-wise training and propose to use a simplified NDCG loss for efficient inference. We demonstrate the generality of our method by applying it to ranking prediction and image retrieval, and show that it outperforms a few state-of-the-art hashing methods.