The goal of this paper is to construct an algebraic algorithm for extracting non-Gaussian subspaces as FastICA/PP and NGCA. For this purpose, we will propose ...
The goal of this paper is to construct an algebraic algorithm for extracting non-Gaussian subspaces as FastICA/. PP and NGCA. For this purpose, we will propose.
The method uses matrices whose non-zero eigen spaces coincide with the non-Gaussian subspace. We also prove its global consistency, that is the true mapping to ...
Joint low-rank approximation for extracting non-Gaussian subspaces · M. Kawanabe, Fabian J Theis · Published in Signal Processing 1 August 2007 · Computer Science, ...
Joint low-rank approximation for extracting non-Gaussian subspaces. record by Fabian J Theis • Joint low-rank approximation for extracting non-Gaussian ...
In the remaining of this section, we will introduce joint low-rank approximation (JLA) of matrices by general- izing this method and show its global ...
Aug 10, 2023 · This paper expands the analysis of randomized low-rank approximation beyond the Gaussian distribution to four classes of random matrices.
Missing: Joint extracting
Kawanabe, M., Theis, F.: Joint low-rank approximation for extracting non-gaussian subspaces. Signal Processing (2007). Google Scholar. Cardoso, J.F.: High ...
Feb 26, 2016 · Transpose and adjoint. Given an m×n matrix A, the transpose At is the n×m matrix B with entries. B(i, j) = A(j, i).
Projection pursuit and non-Gaussian component analysis (NGCA) have been used to find low-rank structure based on maximizing non-Gaussian measures of information ...