オオイシ　クニオ   大石　邦夫    所属   コンピュータサイエンス学部 コンピュータサイエンス学科    職種   教授
言語種別	英語
発行・発表の年月	2022/09
形態種別	学術論文
査読	査読あり
標題	An alternating least-squares algorithm for approximate joint diagonalization of Hermitian matrices
執筆形態	共著
掲載誌名	Digital Signal Processing
掲載区分	国外
出版社・発行元	Elsevier
巻・号・頁	129(Art. 103633),pp.1-12
総ページ数	12
著者・共著者	Shinya Saito and Kunio Oishi
概要	Approximate joint diagonalization (AJD) of a set of target matrices is one of the approaches for blind identification, blind source separation (BSS), and blind source extraction. This approach is useful for applications with measurement noise and/or estimation errors. This paper presents an alternating least-squares (ALS) algorithm for solving AJD problem. The ALS algorithm consists of two processing phases. The first processing phase works for finding diagonal matrices by minimizing the indirect least-squares criterion. A mixing matrix is estimated by minimizing the constrained direct least-squares criterion in the second processing phase. Our ALS algorithm can circumvent numerically unstable behavior by improving the condition number of the estimated mixing matrix in the second processing phase. The proposed algorithm is shown through experimental results to have better estimation performance and faster convergence than existing ALS algorithms with lower computational requirement in the rectangular mixing case.
DOI	10.1016/j.dsp.2022.103633
論文（査読付）ファイル	DOWNLOAD