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In this paper, we develop a new method for prediction O-linked glycosylation site and pattern analysis in protein, which combines independent component analysis (ICA) with a multi-layer neural network (NN). ICA is first used to construct main basis (subspace) of the protein sequence for features extraction. The projections of protein sequence on the subspace with low dimension are used as input data...
This paper proposes an adaptive color independent components based SIFT descriptor (termed CIC-SIFT) for image classification. Our motivation is to seek an adaptive and efficient color space for color SIFT feature extraction. Our work has two key contributions. First, based on independent component analysis (ICA), an adaptive and efficient color space is proposed for color image representation. Second,...
Glycosylation is one of the most important post-translation modifications steps in eukaryotic cell. In this paper, we propose a new approach based on independent component analysis (ICA) for prediction O-linked glycosylation site and pattern analysis. Principal component analysis (PCA) is first used to find significant uncorrelated components, and then ICA is used to extract independent components...
At present there are many methods that could deal well with frontal view face recognition when there is sufficient number of representative training samples. There into, subspace learning method such as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA) are a very hot research topic in this field. However, in some face recognition system, the...
An automatic segmentation system for MR imaging is necessary for studies and 3-dimensional visualization of anatomical structures in many clinical and research applications. Since conventional classification systems use a simple linear classifier, non-linear model is not taken into consideration. In this paper, we propose a new method based on kernel independent component analysis (KICA) for classification...
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