Research Activities

Statistical Study of Artificial Neural Networks

I'm studying artificial neural networks from the standpoint of multivariate data analysis. In particular, I'm interested in automatic selection of network structures and architectures using statistical techniques.

Generative Models for Vision

Generative models are stochastic models about the generation of given data from hidden causes. Estimation algorithms for the parameters of the models are regarded as unsupervised learning algorithms for the internal representation of the data. Using image data, such models can learn visual internal representation fit for the statistical characteristic of the images. I am searching for the minimal generative model to emulate the human vision system.


Mixture of Factor Analyzers

The Mixture of Factor Analyzers (MFA) is one of unsupervised learning models and a straightforward fusion of the mixture-of-Gaussian model and the factor-analysis model. MFA represents a hidden structure in data by a set of local linear manifolds. Since such a framework of hidden structures is very flexible and expansive, it is expected to be the core of adaptive cognition systems.


Bayesian Self-Organizing Map

Self-Organizing Map (SOM) is a unique neural network model which has the ability to extract topological structures hidden in environmental data. Such discovered structures may be useful for the improvement of systems for prediction, classification and control. In reality, SOM is too flexible and fits to any data distributions whichever topology it postulates. I gave this over-flexible model a stiff property and an ability to control this stiffness by itself on a statistical principle. This results in a convenient data-analysis tool without cumbersome trial-and-error tuning.

Demonstrations using java applets and GIF-animations


Previous Studies

I studied mathematical models for various application areas including human-computer interface, image sensing and emotional decision making.


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