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Abstract: An on-line transform domain Least Mean Square (LMS) algorithm based on a neural approach is proposed. A temporal Principal Component Analysis (PCA) network is used as an orthonormalization layer in the transform domain LMS filter. Since PCA learning is an on-line learning algorithm, an on-line transform domain LMS filter can be easily implemented. Moreover, a modified Kalman estimation, which considers the sequence convergence property of the eigenvectors of PCA, is proposed to train the PCA... (Update)
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BibTeX entry: (Update)
@misc{ wang-line,
author = "Chuan Wang and L-K Yen and Jose C. Principe",
title = "On-Line Transform Domain LMS Algorithm Implemented with PCA Learning",
url = "citeseer.ist.psu.edu/444887.html" }
Citations (may not include all citations):815 Adaptive Filter Theory (context) - Haykin - 1991
373 Adaptive Signal Processing (context) - Widrow, Stearns - 1985
111 Optimal unsupervised learning in a single-layer linear feedf.. (context) - Sanger - 1989
103 Theory and application of digital signal processing (context) - Rabiner, Gold - 1975
41 Frequency-domain and multirate adaptive filtering (context) - Shynk - 1992
24 towards faster stochastic gradient search (context) - Darken, Moody - 1992
20 Principal components, minor components, and linear neural ne.. (context) - Oja - 1992
18 Transform domain LMS algorithm (context) - Narayan, Peterson et al. - 1983
13 Reduced complexity echo cancellation using orthonormal funct.. (context) - Davidson, Falconer - 1991
10 Principal components extraction using recursive least square.. (context) - Bannour, Azimi-Sadjadi - 1995
4 the relationships between SVD, KLT, and PCA (context) - Gerbrands
1 the efficiency of the LMS algorithm with nonstationary input.. (context) - Widrow, Walach
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