Title :
Kernel entropy component analysis: New theory and semi-supervised learning
Author_Institution :
Dept. of Phys. & Technol., Univ. of Tromso, Tromso, Norway
Abstract :
A new theory for kernel entropy component analysis (kernel ECA) is developed, based on distribution dependent convolution operators, ensuring the validity of the method for any positive semi-definite kernel. Furthermore, a new semi-supervised kernel ECA classification method is derived with positive results compared to the state-of-the-art.
Keywords :
entropy; learning (artificial intelligence); pattern classification; principal component analysis; ECA classification method; distribution dependent convolution operators; kernel entropy component analysis; semisupervised learning; Convolution; Eigenvalues and eigenfunctions; Entropy; Indexes; Kernel; Principal component analysis; Vectors; Kernel entropy component analysis; classification; convolution operators; semi-supervised; spectral;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on
Conference_Location :
Santander
Print_ISBN :
978-1-4577-1621-8
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2011.6064626
