Metric adaptation for supervised attribute rating

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. Keywords. Supervised feature characterization, adaptive measures.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Marc Strickert, Frank-Michael Schleif, Thomas Villmann
• Keywords: Class-specific Discriminatory Data, Data Similarity Measure, ESANN 2008, Neural Networks, Parametric Pearson Similarity