Minimizing nearest neighbor classification error for nonparametric dimension reduction.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 25050954.
- Also identified by DOI 10.1109/TNNLS.2013.2294547.
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Abstract
In this brief, we show that minimizing nearest neighbor classification error (MNNE) is a favorable criterion for supervised linear dimension reduction (SLDR). We prove that MNNE is better than maximizing mutual information in the sense of being a proxy of the Bayes optimal criterion. Based on kernel density estimation, we derive a nonparametric algorithm for MNNE. Experiments on benchmark data sets show the superiority of MNNE over existing nonparametric SLDR methods.
Medical subject headings
- Algorithms
- Artificial Intelligence
- Bayes Theorem
- Linear Models
- Models, Statistical
- Pattern Recognition, Automated