Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification

• URL: http://arxiv.org/abs/2101.04997v1
• Date: Wed, 13 Jan 2021 10:58:54 GMT
• Title: Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
• Authors: Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, Saketha Nath Jagaralpudi
• Abstract summary: We consider the problem of multi-label classification where the labels lie in a hierarchy. We propose a novel formulation for the joint learning and empirically evaluate its efficacy.
• Score: 9.996804039553858
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We consider the problem of multi-label classification where the labels lie in a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy is known. Encouraged by the recent success of hyperbolic embeddings in capturing hierarchical relations, we propose to jointly learn the classifier parameters as well as the label embeddings. Such a joint learning is expected to provide a twofold advantage: i) the classifier generalizes better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy. We propose a novel formulation for the joint learning and empirically evaluate its efficacy. The results show that the joint learning improves over the baseline that employs label co-occurrence based pre-trained hyperbolic embeddings. Moreover, the proposed classifiers achieve state-of-the-art generalization on standard benchmarks. We also present evaluation of the hyperbolic embeddings obtained by joint learning and show that they represent the hierarchy more accurately than the other alternatives.

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