Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8228(3) 309-316 2013年 [査読有り]
Joint approximate diagonalization (JAD) is a widely-used method for blind source separation, which can separate non-Gaussian sources without any other prior knowledge. In this paper, a new extension of JAD (named ensemble JAD) is proposed in order...
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E95D(2) 596-603 2012年2月 [査読有り]
In order to implement multidimensional scaling (MDS) efficiently, we propose a new method named "global mapping analysis" (GMA), which applies stochastic approximation to minimizing MDS criteria. GMA can solve MDS more efficiently in both the line...
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7553(2) 205-212 2012年 [査読有り]
Joint approximate diagonalization (JAD) is a method solving blind source separation, which can extract non-Gaussian sources without any other prior knowledge. However, it is not robust when the sample size is small because JAD is based on an algeb...
Joint approximate diagonalization (JAD) is one of well-known methods for solving blind source separation. JAD diagonalizes many cumulant matrices of given observed signals as accurately as possible, where the optimization for each pair of signals ...
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