On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems
arXiv:2109.15021
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.
arXiv comment13 pages, 4 Figures, under review
References in corpus (8)
- Manopt, a Matlab toolbox for optimization on manifolds
- Large-scale Multi-label Learning with Missing Labels
- Low-rank optimization for semidefinite convex problems
- Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
- AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
- The Landmark Selection Method for Multiple Output Prediction
- DECAF: Deep Extreme Classification with Label Features
- LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification