Selective information acquisition with application to pattern classification

We propose a selective information acquisition device which can be used to store information selectively according to the importance or characteristics of input patterns. For selective information acquisition, we introduce α-information to distort the ordinary Shannon information function. We maximize or minimize the information to eliminate this distortion. Thus, the distortion elimination can be employed as a basic mechanism of the device to maximize or minimize information selectively. The information device is applied to a phonological feature detection problem. In this problem, experimental results confirmed that conditional information is flexibly maximized or minimized, depending upon input patterns. They also showed that conditional information is a good measure to distinguish between different classes, and that the strength of conditional information is used to classify input patterns