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MOTIVATION Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in(More)
Tumor clustering is becoming a powerful method in cancer class discovery. Nonnegative matrix factorization (NMF) has shown advantages over other conventional clustering techniques. Nonetheless, there is still considerable room for improving the performance of NMF. To this end, in this paper, gene selection and explicitly enforcing sparseness are introduced(More)
This paper proposes a novel and successful method for recognizing palmprint based on radial basis probabilistic neural network (RBPNN) proposed by us. The RBPNN is trained by the orthogonal least square (OLS) algorithm and its structure is optimized by the recursive OLS algorithm (ROLSA). The Hong Kong Polytechnic University (PolyU) palmprint database,(More)
A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l_1-norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR-based method(More)
A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data(More)
A novel neural network technique for nonnegative independent component analysis is proposed in this letter. Compared with other algorithms, this method can work efficiently even when the source signals are not well grounded. Moreover, this method is insensitive to the particular underlying distribution of the source data. Experimental results demonstrate(More)
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(More)