论文信息 - Matrix Estimation Based on Normal Vector of Hyperplane in Sparse Component Analysis

Matrix Estimation Based on Normal Vector of Hyperplane in Sparse Component Analysis

This paper discusses the matrix estimation for sparse component analysis under the k-SCA condition. Here, to estimate the mixing matrix using hyperplane clustering, we propose a new algorithm based on normal vector for hyperplane. Compared with the Hough SCA algorithm, we give a method to calculate normal vector for hyperplane, and the algorithm has lower complexity and higher precision. Two examples demonstrates its performance.

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