464 citations · 538 across the 15 of their papers we have counts for
7 papers · 1 filter
Reconsidering Dependency Networks from an Information Geometry Perspective
Kazuya Takabatake, Shotaro Akaho
Dependency networks (Heckerman et al., 2000) provide a flexible framework for modeling complex systems with many variables by combining independently learned local conditional dist…
Full-Span Log-Linear Model and Fast Learning Algorithm
Kazuya Takabatake, Shotaro Akaho
The full-span log-linear(FSLL) model introduced in this paper is considered an -th order Boltzmann machine, where is the number of all variables in the target system. Let $X…
Reconsidering Dependency Networks from an Information Geometry Perspective
Kazuya Takabatake, Shotaro Akaho
Dependency networks (Heckerman et al., 2000) are potential probabilistic graphical models for systems comprising a large number of variables. Like Bayesian networks, the structure…
On a convergence property of a geometrical algorithm for statistical manifolds
Shotaro Akaho, Hideitsu Hino, Noboru Murata
In this paper, we examine a geometrical projection algorithm for statistical inference. The algorithm is based on Pythagorean relation and it is derivative-free as well as represen…
Constraint-free Graphical Model with Fast Learning Algorithm
Kazuya Takabatake, Shotaro Akaho
In this paper, we propose a simple, versatile model for learning the structure and parameters of multivariate distributions from a data set. Learning a Markov network from a given…
A kernel method for canonical correlation analysis
Shotaro Akaho
Canonical correlation analysis is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features becau…