Dr. Pawan Kumar and his students Jayadev Naram and Tanmay Sinha virtually presented a paper on A Riemannian approach to extreme classification problems at the 9th ACM joint International Conference on Data Science and Management of Data (CODS-COMAD-2022) from 7 – 10 January.
Research work as explained by the authors: We propose a novel Riemannian method called “RXML” 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 a matrix manifold and solved using a Riemannian optimization method. A proof of convergence for the proposed Riemannian optimization method is stated. The proposed approach is tested on several real-world large scale multi-label datasets, and its usefulness is demonstrated through numerical experiments. Experimental results show that RXML improves the trade-off between train time and accuracy. At the similar level of accuracy, the train time of RXML was 1.5 to 4 times faster than that of AnnexML and EXMLDS-4, which are the state-of-the-art embedding-based methods.
CODS-COMAD is a premier international conference focusing on scientific work in Databases, Data Sciences and their applications. Being held for the 5th time as a common conference bringing together the COMAD and the CODS communities, this conference invited researchers in the field of databases, data sciences and their applications to submit their original work.
Conference page: https://cods-comad.in/