RIPML: A Restricted Isometry Property based Approach to Multilabel Learning

TL;DR

RIPML introduces a novel multilabel learning method leveraging the Restricted Isometry Property, projecting labels into a low-dimensional space and using kNN for inference, effectively handling large-scale sparse label data.

Contribution

The paper presents RIPML, a new approach that exploits RIP for efficient multilabel learning with high-dimensional, sparse label spaces, combining random projections and kNN inference.

Findings

RIPML outperforms existing linear dimensionality reduction methods.

The approach effectively handles large-scale, sparse multilabel data.

Extensive simulations validate the method's superiority.

Abstract

The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently. A recurring theme of these problems is that only a few labels are active in any given datapoint as compared to the total number of labels. However, only a small number of existing work take direct advantage of this inherent extreme sparsity in the label space. By the virtue of Restricted Isometry Property (RIP), satisfied by many random ensembles, we propose a novel procedure for multilabel learning known as RIPML. During the training phase, in RIPML, labels are projected onto a random low-dimensional subspace followed by solving a least-square problem in this subspace. Inference is done by a k-nearest neighbor (kNN) based approach. We demonstrate the effectiveness of RIPML by conducting extensive simulations and comparing results with the state-of-the-art linear dimensionality reduction based approaches.