Dimensionality reduction based on ICA for regression problems

Nojun Kwak, Chunghoon Kim, Hwangnam Kim

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


In manipulating data such as in supervised learning, we often extract new features from the original input variables for the purpose of reducing the dimensions of input space and achieving better performances. In this paper, we show how standard algorithms for independent component analysis (ICA) can be extended to extract attributes for regression problems. The advantage is that general ICA algorithms become available to a task of dimensionality reduction for regression problems by maximizing the joint mutual information between target variable and new attributes. We applied the proposed method to a couple of real world regression problems as well as some artificial problems and compared the performances with those of other conventional methods. Experimental results show that the proposed method can efficiently reduce the dimension of input space without degrading the regression performance.

Original languageEnglish
Pages (from-to)2596-2603
Number of pages8
Issue number13-15
Publication statusPublished - 2008 Aug


  • Dimensionality reduction
  • Feature extraction
  • ICA
  • Regression

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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