Abstract
Independent component analysis (ICA) is a technique of transforming observation signals into their unknown independent components; hence ICA has been often applied to blind signal separation problems. In this application, it is expected that the obtained independent components extract essential information of independent signal sources from input data in an unsupervised fashion. Based on such characteristics, ICA is recently utilized as a feature extraction method for images and sounds for recognition purposes. However, since ICA is an unsupervised learning, the obtained independent components are not always useful in recognition. To overcome this problem, we propose a supervised approach to ICA using category information. The proposed method is implemented in a conventional three-layered neural network, but its objective function to be minimized is defined for not only the output layer but also the hidden layer. The objective function consists of the following two terms: one evaluates the kurtosis of hidden unit outputs and the other evaluates the error between output signals and their teacher signals. The experiments are performed for some standard datasets to evaluate the proposed algorithm. It is verified that higher recognition accuracy is attained by the proposed method as compared with a conventional ICA algorithm.
