Local Linear Independent Component Analysis Based on Clustering
暂无分享,去创建一个
[1] G. C. Tiao,et al. Bayesian inference in statistical analysis , 1973 .
[2] Anil K. Jain,et al. Algorithms for Clustering Data , 1988 .
[3] Thomas Martinetz,et al. 'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.
[4] C. Gielen,et al. Neural computation and self-organizing maps, an introduction , 1993 .
[5] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[6] S. Hyakin,et al. Neural Networks: A Comprehensive Foundation , 1994 .
[7] Lucas C. Parra,et al. Statistical Independence and Novelty Detection with Information Preserving Nonlinear Maps , 1996, Neural Computation.
[8] Erkki Oja,et al. A class of neural networks for independent component analysis , 1997, IEEE Trans. Neural Networks.
[9] Geoffrey E. Hinton,et al. Modeling the manifolds of images of handwritten digits , 1997, IEEE Trans. Neural Networks.
[10] Kimmo Valkealahti,et al. Local Independent Component Analysis by the Self-Organizing Map , 1997, ICANN.
[11] Bruno A. Olshausen,et al. Inferring Sparse, Overcomplete Image Codes Using an Efficient Coding Framework , 1998, NIPS.
[12] Juan K. Lin,et al. Faithful Representation of Separable Distributions , 1997, Neural Computation.
[13] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[14] Lei Xu,et al. Bayesian Ying-Yang machine, clustering and number of clusters , 1997, Pattern Recognit. Lett..
[15] Nanda Kambhatla,et al. Dimension Reduction by Local Principal Component Analysis , 1997, Neural Computation.
[16] Te-Won Lee,et al. Independent Component Analysis , 1998, Springer US.
[17] Andrzej Cichocki,et al. Information-theoretic approach to blind separation of sources in non-linear mixture , 1998, Signal Process..
[18] Aapo Hyvärinen,et al. Nonlinear independent component analysis: Existence and uniqueness results , 1999, Neural Networks.
[19] 최승진. Nonlinear dynamic independent component analysis using state-space and neural network models , 1999 .
[20] Erkki Oja,et al. An Experimental Comparison of Neural Algorithms for Independent Component Analysis and Blind Separation , 1999, Int. J. Neural Syst..
[21] Aapo Hyvärinen,et al. Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.
[22] Mark A. Girolami,et al. Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation , 1999 .
[23] Christian Jutten,et al. Source separation in post-nonlinear mixtures , 1999, IEEE Trans. Signal Process..
[24] Terrence J. Sejnowski,et al. Learning Overcomplete Representations , 2000, Neural Computation.
[25] Sejnowski,et al. ICA MIXTURE MODELS FOR UNSUPERVISED CLASSIFICATION AND AUTOMATIC CONTEXT SWITCHING , 2000 .
[26] S. Haykin. Unsupervised adaptive filtering, vol. 1: Blind source separation , 2000 .
[27] D. Donoho. NATURE VS . MATH : INTERPRETING INDEPENDENT COMPONENT ANALYSIS IN LIGHT OF COMPUTATIONAL HARMONIC ANALYSIS , 2000 .
[28] Aapo Hyvärinen,et al. Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.
[29] P. Hoyer,et al. TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS: VISUALIZING THE DEPENDENCE STRUCTURE , 2001 .