Metric adaptation for supervised attribute rating
نویسندگان
چکیده
Abstract. A new approach for faithful relevance rating of attributes is proposed, enabling class-specific discriminatory data space transformations. The method is based on the adaptation of the underlying data similarity measure by using class information linked to the data vectors. For adaptive Minkowski metrics and parametric Pearson similarity, the obtained attribute weights can be used for back-transforming data for further analysis with methods utilizing non-adapted measures as demonstrated for benchmark and mass spectrum data.
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