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A non-negative least squares classifier is proposed in this paper for classifying under-complete data. The idea is that unknown samples can be approximated by sparse non-negative linear combinations of few training samples. Based on sparse coefficient vectors representing the training data, a sparse interpreter can then be used to predict the class label. We have devised new sparse methods which can...
Sparse representation (SR) has been being applied as a state-of-the-art machine learning approach. Sparse representation classification (SRC1) approaches based on norm regularization and non-negative-least-squares (NNLS) classification approach based on non-negativity have been proposed to be powerful and robust. However, these approaches are extremely slow when the size of training samples is...
Classifying genomic and proteomic data is very important to predict diseases in a very early stage and investigate signaling pathways. However, this poses many computationally challenging problems, such as curse of dimensionality, noise, redundancy and so on. The principle of sparse representation has been applied to analyzing high-dimensional biological data within the frameworks of clustering, classification,...
Non-negative factorization (NMF) has been a popular machine learning method for analyzing microarray data. Kernel approaches can capture more non-linear discriminative features than linear ones. In this paper, we propose a novel kernel NMF (KNMF) approach for feature extraction and classification of microarray data. Our approach is also generalized to kernel high-order NMF (HONMF). Extensive experiments...
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