@misc{oai:repo.qst.go.jp:00083373,
 author = {間島, 慶 and 小出(間島)真子 and 八幡, 憲明 and Kei, Majima and Noriaki, Yahata},
 month = {Sep},
 note = {Principal component analysis (PCA) is a widely used statistical tool for extracting low-dimensional structures underlying multivariate data. However, its application to high-dimensional data is limited due to its large computational time. While the conventional PCA algorithm requires polynomial time, using a quantum-inspired algorithm as a subroutine, we have implemented an algorithm that approximates it with computational time proportional to the logarithm of the input dimensionality. The computational efficiency and performance of the implemented algorithm, quantum-inspired PCA, are experimentally evaluated on synthetic and real datasets., 第31回 日本神経回路学会 全国大会（JNNS2021）},
 title = {超高次元データ解析のための量子インスパイア主成分分析の開発},
 year = {2021}
}