Speaker
Keunsu Kim
Description
Nonnegative Matrix Factorization (NMF) is a core tool for uncovering the latent structure of data. Since basis vectors encapsulate the essential features of the data, a central objective of this study is to learn them in a way that is both interpretable and structurally meaningful. In this talk, we quantify the intuition of what constitutes a “good basis” using persistent homology, and introduce Top-NMF, a novel framework that directly incorporates this topological criterion into the optimization process as a regularization term.
Author
Co-authors
Dr
DE JONG VAN LIER
Prof.
KAJI