A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis

@article{Cheng2010ASL,
  title={A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis},
  author={Qiang Cheng},
  journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  year={2010},
  volume={7},
  pages={636-646}
}
Extracting features from high-dimensional data is a critically important task for pattern recognition and machine learning applications. High-dimensional data typically have much more variables than observations, and contain significant noise, missing components, or outliers. Features extracted from high-dimensional data need to be discriminative, sparse, and can capture essential characteristics of the data. In this paper, we present a way to constructing multivariate features and then… CONTINUE READING
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