kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
This paper presents a novel method for interactive exploration of industrial CT volumes such as cast metal parts, with the goal of interactively detecting, classifying, and quantif...
Markus Hadwiger, Laura Fritz, Christof Rezk-Sala...
The matrix, as an extended pattern representation to the vector, has proven to be effective in feature extraction. But the subsequent classifier following the matrix-pattern-orien...
We consider the problem of extracting features for multi-class recognition problems. The features are required to make fine distinction between similar classes, combined with tole...
Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e....