Research Organization of Information and Systems
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    Discovery of invariants in multimodal data
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Japanese
 

Project description

Multimodal data available to us through the Internet and other electronic media are explosively increasing both in number and in variety. To handle such massive data for various purposes, new technologies are in need of development. With this in mind, we have started investigating a new methodology that allows us to discover from multimodal data the information relevant to the purpose at hand (which is referred to as “invariants”). To achieve this goal, we will study several qualitatively different problems from different research areas, in which multimodal data play a central role (e.g., visual/audio/text processing, cognitive science, auditory perception and robotics) . The problems are to be tackled with some of the recently developed inductive learning machines loaded with an automatic model selection mechanism (e.g., Penalized Logistic Regression Machines and Support Vector Machines). The results will be analyzed in order to establish a new methodology for discovery of invariants, which will be applicable to problems across different areas of study .