| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Outline of Final Research Achievements |
The main results of this research project are summarized as follows. (A) We derived a variational Bayesian solution to the sparse matrix factorization problem analytically, and confirmed that the matrix factorization algorithm based on the analytical solution exhibits good performance in seeking a sparse factorized matrix solution. Furthermore, we proposed an automatic tuning method for the sparsity hyperparameter. (B) Based on mathematical neuroscience and thermodynamics, we developed a method to analyze the properties of the solution and the dynamical behavior for the matrix factorization algorithm. (C) We pointed out the effectiveness of sparse matrix factorization for feature extraction from fMRI data of neuronal activity in the brain. In addition, we proposed a method of independent component analysis incorporating sparsity.
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