A hypothesis in the study of the brain is that sparse coding is realized in information representation of external stimuli, which has been experimentally confirmed for visual stimulus recently. However, unlike the specific functional region in the brain, sparse coding in information processing in the whole brain has not been clarified sufficiently. In this study, we investigate the validity of sparse coding in the whole human brain by applying various matrix factorization methods to functional magnetic resonance imaging data of neural activities in the brain. The result suggests the sparse coding hypothesis in information representation in the whole human brain, because extracted features from the sparse matrix factorization (MF) method, sparse principal component analysis (SparsePCA), or method of optimal directions (MOD) under a high sparsity setting or an approximate sparse MF method, fast independent component analysis (FastICA), can classify external visual stimuli more accurately than the nonsparse MF method or sparse MF method under a low sparsity setting.

大脑研究中的一个假设是，外部刺激的信息表示实现了稀疏编码，这一点最近在视觉刺激中得到了实验证实。然而，与大脑的特定功能区域不同，整个大脑信息处理的稀疏编码尚未得到充分阐明。在本研究中，我们通过将各种矩阵分解方法应用于大脑神经活动的功能磁共振成像数据，研究稀疏编码在整个人脑中的有效性。结果表明了整个人脑信息表示中的稀疏编码假设，因为在高稀疏性设置下通过稀疏矩阵分解（MF）方法、稀疏主成分分析（SparsePCA）或最佳方向方法（MOD）提取特征或者近似稀疏MF方法，快速独立分量分析（FastICA），可以在低稀疏性设置下比非稀疏MF方法或稀疏MF方法更准确地对外部视觉刺激进行分类。