Matchability-oriented feature selection for recognition structure learning

For effective recognition, a recognition structure that controls the information flow among the specialized processing modules should reflect the implicit correlation structure of the environmental input. Autonomous construction of a recognition structure will lead to extensive improvement in the flexibility of the adaptive recognition system. For this purpose we propose a matchability-oriented feature selection that can collect highly correlated features at each local module. Conventional techniques, which collect features that are more independent, are not suitable. Matchability is a concept derived from the recognition functions of an adaptive intelligent agent (useful for action generation) and corresponds to the probability of input data items matching stored data items in the recognition system. The proposed algorithm changes the weights attached to each feature depending on the degree of matchability of each feature. This algorithm could select highly correlated features in simple simulation.