Advances in neural networks - ISNN 2008 5th International by Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu

By Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu

The quantity set LNCS 5263/5264 constitutes the refereed complaints of the fifth foreign Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.

The 192 revised papers offered have been rigorously reviewed and chosen from a complete of 522 submissions. The papers are geared up in topical sections on computational neuroscience; cognitive technology; mathematical modeling of neural structures; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic tools; supervised studying; unsupervised studying; aid vector desktop and kernel tools; hybrid optimisation algorithms; laptop studying and information mining; clever regulate and robotics; trend acceptance; audio photo processinc and computing device imaginative and prescient; fault analysis; functions and implementations; purposes of neural networks in digital engineering; mobile neural networks and complex keep watch over with neural networks; nature encouraged  tools of high-dimensional discrete facts research; development attractiveness and knowledge processing utilizing neural networks.

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Extra info for Advances in neural networks - ISNN 2008 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008: proceedings

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As a powerful data modeling tool for pattern recognition, multilinear algebra of the higher order tensors has been proposed as a potent mathematical framework to manipulate the multiple factors underlying the observations. Currently common tensor decomposition methods include: (1) the CANDECOMP/PARAFAC model [11,12,13]; (2) the Tucker Model[14,15]; (3) Nonnegative Tensor Factorization (NTF) which imposes the nonnegative constraint on the CANDECOMP/PARAFAC model [16,17]. In computer vision applications, Multilinear ICA [18]and tensor discriminant analysis [19] are applied to image representation and recognition, which improve recognition performance.

We also impose orthogonal constraint to cNTF which helps to extract the helpful feature by minimizing the redundancy of different basis functions. 4 Experiments Results In this section we provide the evaluation results of a speaker identification system using ANTF. Aurora2 speech corpus is used to test the recognition performance, which is designed to evaluate speech recognition algorithms in noisy conditions. Different noise classes were considered to evaluate the performance of ANTF against MFCC, MelNMF, Mel-PCA feature and identification accuracy was assessed.

The same idea can be applied to the NTF. Then the corresponding cost functions with orthogonal and sparse control constraints can be given by ⎛ ⎞ Nd Nd¯ M 2 1 ⎝ [X(d) ]pq − [A(d) SZ(d) ]pq + α JLS2 (A(d) ) = [A(d)T A(d) ]pq ⎠ 2 p=1 q=1 d=1 M JKL2 (A(d) ) = d=1 p=q ⎛ ⎝ (9) Nd Nd¯ [X(d) ]pq log p=1 q=1 ⎞ [A(d)T A(d) ]pq ⎠ +α [X(d) ]pq − [X(d) ]pq + [A(d) SZ(d) ]pq [A(d) SZ(d) ]pq (10) p=q where α > 0 is a balancing parameter between reconstruction and orthogonality. We can derive multiplicative learning algorithms for mode matrices A(d) using the exponential gradient, which are similar to those in NMF.

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