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stat.ME2022
Automatic sparse PCA for high-dimensional data
Kazuyoshi Yata, Makoto Aoshima
Sparse principal component analysis (SPCA) methods have proven to efficiently analyze high-dimensional data. Among them, threshold-based SPCA (TSPCA) is computationally more cost-e…
astro-ph.IM2022★ 1 cited
High Dimensional Statistical Analysis and its Application to ALMA Map of NGC 253
Tsutomu T. Takeuchi, Kazuyoshi Yata, Kento Egashira +6
In astronomy, if we denote the dimension of data as and the number of samples as , we often meet a case with . Traditionally, such a situation is regarded as ill-po…