Chun-Hou Zheng

Feature selection in independent component subspace for microarray data classification

Abstract A novel method for microarray data classification is proposed in this letter. In this scheme, the sequential floating forward selection (SFFS) technique is used to select the independent components of the DNA microarray data for classification. Experimental results show that the method is efficient and feasible.

Letters: Optimal selection of time lags for TDSEP based on genetic algorithm

In this letter, a two-step learning scheme for the optimal selection of time lags is proposed for a typical temporal blind source separation (TBSS), Temporal Decorrelation source SEParation algorithm (abbreviated as TDSEP). Given the time lags, the time-delayed second-order correlation matrices are first diagonalized simultaneously. Then, a genetic algorithm is used to update the time lags. Finally, experimental results demonstrate that the proposed method can efficiently accomplish the aforementioned task.

Bark classification based on textural features using artificial neural networks

In this paper, a new method for bark classification based on textural and fractal dimension features using Artificial Neural Networks is presented. The approach involving the grey level co-occurrence matrices and fractal dimension is used for bark image analysis, which improves the accuracy of bark image classification by combining fractal dimension feature and structural texture features on bark image. Furthermore, we have investigated the relation between Artificial Neural Network (ANN) topologies and bark classification accuracy. Furthermore, the experimental results show the facts that this new approach can automaticly identify the Tplants categories and the classification accuracy of the new method is better than that of the method using the nearest neighbor classifier.

Letters: Using batch algorithm for kernel blind source separation

By combining the batch algorithm with the kernel trick, an improved kernel blind source separation (IKBSS) is presented. The IKBSS has not only a better performance but also a less computational complexity compared to the original kernel blind source separation (KBSS).

Gene Expression Data Classification Using Independent Variable Group Analysis

Microarrays are capable of detecting the expression levels of thousands of genes simultaneously. In this paper, a new method for gene selection based on independent variable group analysis is proposed. In this method, we first used t-statistics method to select a part of genes from the original data. Then we selected the key genes from the selected genes by t-statistics for tumor classification using IVGA. Finally, we used SVM to classify tumors based on the key genes selected using IVGA. To validate the efficiency, the proposed method is applied to classify three different DNA microarray data sets. The prediction results show that our method is efficient and feasible.

Nonlinear blind source separation using hybrid neural networks

This paper proposes a novel algorithm based on minimizing mutual information for a special case of nonlinear blind source separation: post-nonlinear blind source separation. A network composed of a set of radial basis function (RBF) networks, a set of multilayer perceptron and a linear network is used as a demixing system to separate sources in post-nonlinear mixtures. The experimental results show that our proposed method is effective, and they also show that the local character of the RBF network’s units allows a significant speedup in the training of the system.

Image feature extraction based on an extended non-negative sparse coding neural network model

This paper proposes an extended non-negative sparse coding (NNSC) neural network model for natural image feature extraction. The advantage for our model is to be able to ensure to converge to the basis vectors, which can respond well to the edge of the original images. Using the criteria of objective fidelity and the negative entropy, the validity of image feature extraction is testified. Furthermore, compared with independent component analysis (ICA) technique, the experimental results show that the quality of reconstructed images obtained by our method outperforms the ICA method.

Natural image compression using an extended non-negative sparse coding neural network technique

This paper proposes an extended non-negative sparse coding (NNSC) neural network method for image compression. This method can exploit the NNSC algorithm to obtain transform-based compression schemes adapted to standard natural image classes, which results from the statistical properties of natural image data. In particular, several methods of image compression such as linear principal component analysis (PCA), wavelet-based analysis, independent component analysis (ICA), etc., are evaluated and compared based on both the standard signal to noise ratio (SNR) and picture quality scale (PQS) criteria. The simulation results show that, in the case of using a fixed block by block scanning a natural image randomly, the quality of a compressed image obtained by our extended NNSC compression algorithm indeed outperforms the one obtained by other algorithms mentioned above.

Blind inversion of Wiener system for single source using nonlinear blind source separation

In this paper, a nonlinear blind source separation system with post-nonlinear mixing; model, and an unsupervised learning algorithm for the parameters of this separating system are presented for blind inversion of Wiener system for single source. The proposed method firstly changes the deconvolution part of Wiener system into a special case of linear blind source separation (BSS). Then the nonlinear BSS system is applied to derive the source signal. The proposed nonlinear BSS method can dynamically estimate the nonlinearity of mixing model and adapt to the cumulative probability function (CPF) of sources. Finally, experimental results demonstrate that our proposed method is effective and efficient for the problems addressed.