ICA [6] ICA) [7, 8] ICA ICA ICA [9, 10] J-F. Cardoso. [13] Matlab ICA. Comon[3], Amari & Cardoso[4] ICA ICA

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Transcription:

16 1 (Independent Component Analysis: ICA) 198 9 ICA ICA ICA 1 ICA 198 Jutten Herault Comon[3], Amari & Cardoso[4] ICA Comon (PCA) projection persuit projection persuit ICA ICA ICA 1 [1] [] ICA ICA EEG [] fmri [6] [7, 8] [9, 1] J-F. Cardoso JADE [11] A. Hyvärinen FastICA [1] ICA central web page [13] Matlab ( ) ICA ICA ICA ICA 1

ICA.1 ICA. ICA 3 [14, 1] s(t) =(s 1 (t),...,s n (t)) T t =, 1,,... s(t) T x(t) =(x 1 (t),...,x m (t)) T t =, 1,,... x m n s(t) x(t) x(t) =As(t), (1) ( 1) A m n ICA s(t) A x(t) n s s 1 a 1 a 1 a 11 a 1: ICA n m n m W y(t) =W x(t), () y(t) WA= I (I n n ) y(t) s(t) y(t) WA = PD (P 1 n n D n n ) x x 1 JADE FastICA ICA s i () W x y y p(y)=p(y 1,,y n ) W y y i p(y i ) y i p(y) = n p(y i ) i=1 p(y) n i=1 p(y i) W p(y) n i=1 p(y i) Kullback-Leibler ( ) KL KL(W) = p(y) p(y) log n i=1 p(y dy i) (3) n = H(Y ; W )+ H(Y i ; W ). i=1 H(Y ; W ) H(Y i ; W ) p(y)dy = p(x)dx p(y) =p(x)/ W ( W W ) H(Y ; W ) H(Y i ; W ) p(x) W

H(Y ; W )= = p(y) log p(y)dy p(x)(log p(x) log W )dx x(t) = H(X) + log W, H(Y i ; W )= p(y) log p(y i )dy = p(x) log p(y i )dx KL(W) p(y i ) KL(W) W KL(W) W = ( (W T ) 1 E x [ϕ(y)x T ] ) = ( I E x [ϕ(y)y T ] ) (W T ) 1 (4) ( log p(y1 ) ϕ(y) =,..., log p(y ) T n) y 1 y n W (4) (W T ) 1 W T W [16] W ( I E x [ϕ(y)y T ] ) W () [17] η W W t+1 = W t + η ( I ϕ(y)y T) W t. ϕ(y) W [4] W ϕ(y) ICA x(t)x(t + τ) T = A s(t)s(t + τ) T A T R s1 (τ) = A... AT, R sn (τ) x(t) R si (τ) s i (t) s(t) τ s i (t) W y(t) y(t)y(t + τ) T = (WAs(t)) (WAs(t + τ)) T λ 1 R s 1 (τ) =..., λ n R s n (τ) 1,,...,n 1,,...,n λ i W τ y(t) x(t) τ i W [18] W x(t)x(t + τ i ) T W T = Λ i, i =1,...,r, (6) Λ i W [18] τ i [1] KL [19] 3

s i (t) [] { Q(t) = 1 n } log E[yi (t)] log det E[y(t)y(t) T ], i=1 (7) (E[ ] ) Q ICA Q E[y(t)y(t) T ] W W Q Q W 3 ICA ICA 3.1 MEG [1, ] MEG 1 MEG (1msec) ( mm) (1 14 T) MEG ( ) ( 1) 1 T Hz 1 14 a few 1 4 a few 1 () 1 11 1 13 or 6 1 14 1: Low-Pass 1 ft x 1 x x 3 1 1 3 4 time(msec) : MEG : 1 1 3 1msec 41msec 1 msec ICA x x = As 1 s 4

ICA x = As + ɛ ɛ ( ) x = As + ɛ = A s s =(A, I) ɛ ICA ɛ [] ICA A ɛ ICA 3 [3, 4] y 1 y y 3 y 4 y y 6 y 7 y 8 y 9 y 1 y 11 y 1 y 13 y 14 y 1 y 16 y 17 1 1 3 4 time(msec) 3: 3. 3 ICA MEG (artifact) artifact 3 y 9 18Hz y 11 41msec 3 ft x 1 x x 3 1 1 3 4 time(msec) 4:

MEG 4 ( 1msec msec) ( 8msec 1msec) MEG [] ICA 4 fmri ICA ( ) ICA ICA : MEG 1msec 8 1msec V1 ICA ICA 4 ICA ICA MEG, EEG, fmri 6

x(t) =A(t) s(t) x i (t) = a ik (t) s k (t), k a ik (t) s k (t) = a ik (τ)s k (t τ), τ= ( 6) (FIR ) s s 1 a (t) 1 1 a (t) a (t) 11 a (t) 6: W y(t) W (t) y(t) =W (t) x(t) W (t) ={w ij (t)} FIR (4) (7) [1, 6] [7, 1, 8] x(t), s(t), A(t) Fourier ω ˆx(ω), ŝ(ω), Â(ω) ˆx(ω) =Â(ω)ŝ(ω), msec x x 1 ˆx(ω, t s )=Â(ω)ŝ(ω, t s), ˆx(ω, t s ) ŝ(ω, t s ) x(t), s(t) windowed Fourier ω (1) (1) ICA W (ω) W (ω) 1 A(ω) W (ω) W (ω) 1 [8, 1] ICA ICA 3 4 ICA ICA 7

ICA ICA s A s s i [9] A A [3, 8] ICA [31] KL [3, 33] [34] 6 ICA web [3, 36, 37] 3 [38, 39] ICA ICA 1999 1 1 3 ICA ICA1 [4] 3 4 4 ICA [41] 7 [1] Jutten, C. and Herault, J. (1991): Separation of sources, Part I, Signal Processing, Vol. 4, No. 1, pp. 1 1. [] Jutten, C. and Taleb, A.: Source separation: from dusk till dawn, in Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA), pp. 1 6 (). [3] Comon, P. (1994): Independent component analysis, A new concept?, Signal Processing, Vol. 36, No. 3, pp. 87 314. [4] Amari, S. and Cardoso, J.-F. (1997): Blind Source Separation Semiparametric Statistical Approach, 3 8

IEEE Transactions on Signal Processing, Vol. 4, No. 11, pp. 69 7. [] Makeig, S., Jung, T.-P., Bell, A. J., Ghahremani, D. and Sejnowski, T. J. (1997): Blind Separation of Auditory Event-related Brain Responses into Independent Components, Proc. Natl. Acad. Sci. USA, pp. 1979 1984. [6] Mckeown, M. J., Jung, T.-P., Makeig, S., Brown, G., Kindermann, S. S., Lee, T.-W. and Sejnowski, T. J.: Spatially independent activity patterns in functional magnetic resonance imaging data during the Stroop color-naming task, in Proceedings of the National Academy of Sciences, Vol. 9, pp. 83 81 (1998). [7] Bell, A. J. and Sejnowski, T. J. (1997): The independent component of natural scenes are edge filters, Vison Research, Vol. 37, pp. 337 3338. [8] Olshausen, B. A. and Field, D. J. (1996): Emergence of simple-cell receptive field properties by learning a sparce code for natural images, Nature, Vol. 381, pp. 67 69. [9] Bell, A. J. and Sejnowski, T. J. (199): An information maximization approach to blind separation and blind deconvolution, Neural Computation, Vol. 7, No. 6, pp. 119 119. [1] Douglas, S. C. and Cichocki, A. (1997): Neural Networks for Blind Decorrelation of Signals, IEEE Transactions on Signal Processing, Vol. 4, No. 11, pp. 89 84. [11] http://www.tsi.enst.fr/icacentral/algos/cardoso/. [1] http://www.cis.hut.fi/projects/ica/fastica/. [13] http://www.tsi.enst.fr/icacentral/. [14] Cardoso, J.-F.: The three easy routes to independent component analysis; contrasts and geometry, in Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA1), pp. 1 6 (1). [1] Murata, N., Ikeda, S. and Ziehe, A. (1): An Approach to Blind Source Separation Based on Temporal Structure of Speech Signals, Neurocomputing, Vol. 41, No. 1-4, pp. 1 4. [16] Amari, S., Cichocki, A. and Yang, H. H.: A New Learning Algorightm for Blind Signal Separation, in Touretzky, D. S., Mozer, M. C. and Hasselmo, M. E. eds., Advances in Neural Information Processing Systems, Vol. 8, pp. 77 763, The MIT Press, Cambridge MA (1996). [17] Amari, S., Chen, T. and Cichocki, A. (1997): Stability Analysis of Learning Algorithms for Blind Source Separation, Neural Networks, Vol. 1, No. 8, pp. 134 131. [18] Molgedey, L. and Schuster, H. G. (1994): Separation of a mixture of independent signals using time delayed correlations, Phys. Rev. Lett., Vol. 7, No. 3, pp. 3634 3637. [19] Amari, S.: ICA of temporally correlated signals learning algorithm, in Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 99), pp. 13 18 (1999). [] Matsuoka, K., Ohya, M. and Kawamoto, M. (199): A Neural Net for Blind Separation of Nonstationary Signals, Neural Networks, Vol. 8, No. 3, pp. 411 419. [1] Ikeda, S.: ICA on Noisy Data: A Factor Analysis Approach, in Girolami, M. ed., Advances in Independent Component Analysis, chapter 11, pp. 1 1, Springer-Verlag London Ltd. (). [] Ikeda, S. and Toyama, K. (): Independent Component Analysis for Noisy Data MEG data analysis, Neural Networks, Vol. 13, No. 1, pp. 163 174. [3] Cao, J., Murata, N., Amari, S., Cichocki, A. and Takeda, T.: A Robust ICA Approach for unaveraged single-trial auditory evoked fields data decomposition, in Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA1), pp. 44 4 (1). [4] Kawanabe, M. and Murata, N.: Independent Component Analysis in the Presence of Gaussian noise based on estimating functions, in Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA), pp. 39 44 (). [] Toyama, K., Yoshikawa, K., Yoshida, Y., Kondo, Y., Tomita, S., Takanashi, Y., Ejima, Y. and Yoshizawa, S. (1999): A new method for magnetoencephalography: A three dimensional magnetometerspatial filter system, Neuroscience, Vol. 91, No., pp. 4 41. [6] Kawamoto, M., Matsuoka, K. and Ohnishi, N. (1998): A method of blind separation for convolved non-stationary signals, Neurocomputing, Vol., No. 1-3, pp. 17 171. [7] Smaragdis, P. (1998): Blind separation of convolved mixtures in the frequency domain, Neurocomputing, Vol., No. 1-3, pp. 1 34. [8] Ikeda, S. and Murata, N.: A method of blind separation based on temporal structure of signals, in Proceedings of 1998 International Conference on Neural Information Processing (ICONIP 98), Vol., pp. 737 74, Kitakyushu, Japan (1998). [9] Rickard, S., Balan, R. and Rosca, J.: Real-Time Time-Frequency Based Blind Source Separation, in Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA1), pp. 61 66 (1). [3] Hyvärinen, A., Hoyer, P. and Inki, M. (1): Topographic Independent Component Analysis, Neural Computation, Vol. 13, No. 7, pp. 1 18. 9

[31] Bach, F. R. and Jordan, M. I.: Kernel Independent Component Analysis, Technical Report UCB//CSD-1-1166, University of California, Berkeley, (1). [3] Matsuyama, Y., Katsumata, N. and Imahara, S.: Convex Divergence as a Surrogate Function for Independence: The f-divergence ICA, in Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA1), pp. 31 36 (1). [33] Minami, M. and Eguchi, S.: Robust Blind Source Separation by β-divergence, to appear in Neural Computation (). [34] Akuzawa, T.: New Fast Factorization Method for Multivariate Optimization and its Realization as ICA Algorithm, in Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA1), pp. 114 119 (1). [3] Lee, T.-W. (1998): Independent Component Analysis, Kluwer Academic Publishers. [36] Hyvärinen, A., Karhunen, J. and Oja, E. (1): Independent Component Analysis, John Wiley & Sons, Inc. [37] Cichocki, A. and Amari, S. (): Adaptive Blind Signal and Image Processing, John Wiley & Sons, Inc. [38] Girolami, M. ed. (): Advances in Independent Component Analysis, Springer-Verlag London Ltd. [39] Haykin, S. ed. (): Unsupervised Adaptive Filtering, Volume 1, Blind Source Separation, Vol. 1, John Wiley & Sons, Inc. [4] http://www.ica1.org. [41] http://ica3.jp. 1