|Budget Amount *help
¥3,500,000 (Direct Cost : ¥3,500,000)
Fiscal Year 2002 : ¥1,300,000 (Direct Cost : ¥1,300,000)
Fiscal Year 2001 : ¥2,200,000 (Direct Cost : ¥2,200,000)
Independent Component Analysis (ICA) is a method to estimate unknown independent components which generate observed signals. In this research, the convex divergence was selected as the performance criterion for the independence. This measure is the source of the generalized logarithm. The obtained algorithm is named the f-ICA. The f-ICA contains the minimum mutual information ICA as a special case. The f-ICA can be realized as (a) the momentum method which adds the previous increment, and (b) the look-ahead method which adds the estimated future increment. Both methods show several times faster speed than the minimum mutual information method at the cost of a few additional memory. Thus, the first part of this project was successful by giving the accelerated ICA algorithm and novel properties of statistical measures related to the generalized logarithm.
In addition to the theoretical sophistication, the following experimental results are successfully obtained in this project:
(i) In any ICA algorithms, permutation indeterminacy is unavoidable. Users are obliged to check every independent component after the convergence of the algorithm. The investigator presented a way to inject prior knowledge as a regularization term. By this method, the most important component always appears as the first one.
(ii) A software system was created, which is beyond a laboratory level, i.e., a more general user level.
(iii) By using the above software system, human brain's functional maps are successfully obtained; (a) the main area of moving image recognition (dorsal occipital cortex), and (b) a separation of V1 and V2 regions of visual areas.