Title :
Principal curve classifier-a nonlinear approach to pattern classification
Author :
Chang, Kui-yu ; Ghosh, Joydeep
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Abstract :
Presents a general nonlinear approach to pattern classification using principal curves. Principal curves are nonparametric, nonlinear generalizations of the first principal component, and may also be regarded as continuous versions of 1-D self-organizing maps. The new classification technique, principal curve classifier (PCC), involves a novel way of computing a principal curve for each class using the class-labeled training data. An unlabeled test point is given the class-label of the principal curve that is closest to it in Euclidean distance. Preliminary experiments comparing the PCC with established classification methods, using selected datasets from the Elena and Proben1 benchmarks, highlight the merits and limitations of this algorithm
Keywords :
learning (artificial intelligence); nonparametric statistics; pattern classification; self-organising feature maps; 1D self-organizing maps; Elena; Euclidean distance; Proben1; class-labeled training data; first principal component; nonlinear approach; pattern classification; principal curve classifier; unlabeled test point; Benchmark testing; Euclidean distance; Kernel; Laboratories; Machine learning; Machine learning algorithms; Pattern classification; Self organizing feature maps; Smoothing methods; Training data;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.682365
