Abstract
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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Gao, F., Sun, G., Xiao, M., Lv, J. (2010). Matrix Estimation Based on Normal Vector of Hyperplane in Sparse Component Analysis. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_23
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DOI: https://doi.org/10.1007/978-3-642-13498-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13497-5
Online ISBN: 978-3-642-13498-2
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