3 papers
stat.ML2024
EM: Double Bounded -Divergence Optimization for Tensor-based Discrete Density Estimation
Kazu Ghalamkari, Jesper Løve Hinrich, Morten Mørup
Tensor-based discrete density estimation requires flexible modeling and proper divergence criteria to enable effective learning; however, traditional approaches using -divergenc…
stat.ML2021
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
Kazu Ghalamkari, Mahito Sugiyama
We present an efficient low-rank approximation algorithm for non-negative tensors. The algorithm is derived from our two findings: First, we show that rank-1 approximation for tens…
stat.ML2020
Fast Rank Reduction for Non-negative Matrices via Mean Field Theory
Kazu Ghalamkari, Mahito Sugiyama
We propose an efficient matrix rank reduction method for non-negative matrices, whose time complexity is quadratic in the number of rows or columns of a matrix. Our key insight is…