In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying ...
Abstract. In this paper we elaborate on the challenges of learning man- ifolds that have many relevant clusters, and where the clusters can have.
In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying statistics.
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(2009) Learning highly structured manifolds: harnessing the power of SOMs. Chapter in “Similarity based clustering”, Lecture Notes in Computer Science (Eds ...
This review focuses on the last decade, in order to provide an overview of the main evolution of the seminal SOM algorithm as well as of the methodological ...
Learning highly structured manifolds: Harnessing the power of SOMs. In M. Biehl, B. Hammer, M. Verleysen, & T. Villmann (Eds.), Lecture notes in computer ...
Taşdemir, L. Zhang (2009) Learning highly structured manifolds: harnessing the power of SOMs. In “Similarity based clustering”, Lecture Notes in.
Jul 9, 2025 · Manifold optimization (MO) is a powerful mathematical framework that can be applied to solving complex optimization problems with objective ...
In this thesis, we study how statistical models can be generalized on these curved spaces, also we develop methods to learn the Riemannian manifold directly ...
Learning Highly Structured Manifolds: Harnessing the Power of SOMs · E. MerényiK. TasdemirLili Zhang. Computer Science, Mathematics. Similarity-Based Clustering.