文件列表:

kwiener\demo_kwiener.m (4764, 2008-02-16)
kwiener\kernelg.m (1105, 2008-02-16)
kwiener\kwiener_predict.m (3677, 2008-02-16)
kwiener\kwiener_train.m (3356, 2008-02-16)
kwiener\mycol2im.m (420, 2008-02-16)
kwiener\usps.mat (2608972, 2007-12-25)
kwiener (0, 2009-10-30)

Name: Kernel Wiener Filter (kernel Dependency Estimation) Author: Makoto Yamada (myamada@ism.ac.jp) Date: 12/25/2007 ############################################################ The following code implements a kernel Wiener Filter (kernel Dependency Estimation) algorithm in MATLAB. Note: The algorithm dependes on the eigenvalue decomposition, thus only a few thousand of data samples for training dataset is applicable so far. (In the near future, we will expand the algorithm for large scale data.) There are 4 main functions: 1. kwiener_train -- Compute the kernel Wiener filter coefficient. 2. kwiener_predict -- Compute the pre-image using kernel Wiener filter. 3. kernelg -- Compute the kernel Gram matrix using Gaussian kernel. 4. demo_kwiener -- Run the USPS filtering problem using kernel Wiener filter. Following references were related to the code: 1. J. Weston et.al. ``Kernel Dependency Estimation,'' NIPS 2003 2. M. Yamada and M. R. Azimi-Sadjadi, ``Kernel Wiener Filter using Canonical Correlation Analysis Framework,'' IEEE SSP'05, Bordeaux, France, July 17-20, 2005. 3. C. Cortes et.al. ``A General Regression Technique for Learning Transductions,'' ICML, Bonn, Germany, Aug 7-11. 4. M. Yamada and M. R. Azimi-Sadjadi, ``Kernel Wiener Filter with Distance Constraint,'' ICASSP, Toulouse, France, May 14-19, 2006 5. Zhe Chen et.al, ``Correlative Learning: A Basis for Brain and Adaptive Systems,'' Wiley, Oct. 2007 (Section 4.6) ############################################################ Example Usage: %Training kernel Wiener filter between input n times N dimensional matrix X %and output m times N dimensional matrix Y with the use of Gaussian kernel. >load usps; >param.s = 256*0.7; %Gaussian Kernel Parameter >kcoeff = kwiener_train(X,Y,param.s); %Computing the pre-image of given unknown vector "ninput". >param.nnear = 20; %Number of nearest nighbor vectors to estimate a pre-image. >param.rnk = 100; %Rank of kernel Wiener filter. >ninput = Ytest(:,1); %Unknown input >pimage = kwiener_predict(ninput,X,Y,kcoeff,param);%Predicting the preimage of "ninput". ######################################################## Changes: 02/16/08 Rev1: Modified kwiener_predict (change the pre-image computation)

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