Md. Nurul Haque Mollah

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Independent component analysis (ICA) aims to extract original independent signals (source components) that are linearly mixed in a basic framework. This paper discusses a sequential procedure for hidden class separation in which the observed data follow a mixture of several ICA models. Each class is described by linear combination of independent and(More)
This paper discusses a new highly robust learning algorithm for exploring local principal component analysis (PCA) structures in which an observed data follow one of several heterogeneous PCA models. The proposed method is formulated by minimizing beta-divergence. It searches a local PCA structure based on an initial location of the shifting parameter and a(More)
BACKGROUND Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide(More)
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