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As the emergement of high-throughput measurement technologies, we are entering the big data era. Modern data are often generated from heterogeneous multiple sources, thus can be called multi-view data. The challenge of effectively integrating such data for decision making and novel knowledge discovery is raised. Matrix factorization methods have historically played important roles in various analyses...
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...
Non-negative information can benefit the analysis of microarray data. This paper investigates the classification performance of non-negative matrix factorization (NMF) over gene-sample data. We also extends it to higher-order version for classification of clinical time-series data represented by tensor. Experiments show that NMF and the higher-order NMF can achieve at least comparable prediction performance.
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