On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems

• URL: http://arxiv.org/abs/2109.15021v1
• Date: Thu, 30 Sep 2021 11:28:35 GMT
• Title: On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems
• Authors: Jayadev Naram, Tanmay Kumar Sinha, Pawan Kumar
• Abstract summary: A constrained optimization problem is formulated as an optimization problem on matrix manifold. The proposed approach is tested on several real world large scale multi-label datasets.
• Score: 2.7436792484073638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel Riemannian method for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models. A constrained optimization problem is formulated as an optimization problem on matrix manifold and solved using a Riemannian optimization method. The proposed approach is tested on several real world large scale multi-label datasets and its usefulness is demonstrated through numerical experiments. The numerical experiments suggest that the proposed method is fastest to train and has least model size among the embedding-based methods. An outline of the proof of convergence for the proposed Riemannian optimization method is also stated.

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