Title :
Self-stabilized minor subspace extraction algorithm based on Householder transformation
Author :
Abed-Meraim, K. ; Attallah, S. ; Chkeif, A. ; Hua, Y.
Author_Institution :
TSI Dept., Telecom Paris, France
Abstract :
In this paper, we propose an orthogonalized version of Oja´s algorithm (OOja) that can be used for the estimation of minor and principal subspaces of a vector sequence. The new algorithm offers, as compared to Oja, such advantages as orthogonality of the weight matrix, which is ensured at each iteration, numerical stability and a quite similar computational complexity
Keywords :
computational complexity; iterative methods; matrix algebra; numerical stability; principal component analysis; signal processing; Householder transformation; computational complexity; iteration; minor subspace estimation; numerical stability; orthogonalized Oja´s algorithm; principal subspace estimation; self-stabilized minor subspace extraction algorithm; vector sequence; weight matrix orthogonality; Computational complexity; Covariance matrix; Equations; Error correction; Numerical stability; Principal component analysis; Telecommunications; Wireless communication;
Conference_Titel :
Statistical Signal and Array Processing, 2000. Proceedings of the Tenth IEEE Workshop on
Conference_Location :
Pocono Manor, PA
Print_ISBN :
0-7803-5988-7
DOI :
10.1109/SSAP.2000.870088
