1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Many-body Approximation for Non-negative Tensors
Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara
We present an alternative approach to decompose non-negative tensors, called many-body approximation. Traditional decomposition methods assume low-rankness in the representation, r…
Fast Rank-1 NMF for Missing Data with KL Divergence
Kazu Ghalamkari, Mahito Sugiyama
We propose a fast non-gradient-based method of rank-1 non-negative matrix factorization (NMF) for missing data, called A1GM, that minimizes the KL divergence from an input matrix t…
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…
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…