Fast Multi-label Learning

TL;DR

This paper introduces a simple, scalable stochastic sketch method for multi-label classification that offers competitive performance with theoretical guarantees, addressing the computational challenges of existing embedding approaches.

Contribution

It proposes a novel stochastic sketch strategy for multi-label learning with provable guarantees, improving scalability over traditional embedding methods.

Findings

The method achieves competitive accuracy compared to complex embedding techniques.

Theoretical guarantees support the effectiveness of the stochastic sketch approach.

Empirical results demonstrate the method's superiority in large-scale applications.

Abstract

Embedding approaches have become one of the most pervasive techniques for multi-label classification. However, the training process of embedding methods usually involves a complex quadratic or semidefinite programming problem, or the model may even involve an NP-hard problem. Thus, such methods are prohibitive on large-scale applications. More importantly, much of the literature has already shown that the binary relevance (BR) method is usually good enough for some applications. Unfortunately, BR runs slowly due to its linear dependence on the size of the input data. The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process. To achieve our goal, we provide a simple stochastic sketch strategy for multi-label classification and present theoretical results from both algorithmic and statistical learning perspectives. Our comprehensive empirical studies corroborate our theoretical findings and demonstrate the superiority of the proposed methods.