Title :
Unsupervised learning and generalization
Author :
Hansen, Lars Kai ; Larsen, Jan
Author_Institution :
Sect. for Digital Signal Process., Tech. Univ., Lyngby, Denmark
Abstract :
The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy with supervised learning. The empirical and analytical estimates are compared for principal component analysis and for K-means clustering based density estimation
Keywords :
unsupervised learning; K-means clustering based density estimation; generalization error; principal component analysis; unsupervised learning machines; Cost function; Digital signal processing; Mathematical model; Noise robustness; Principal component analysis; Stochastic processes; Supervised learning; Testing; Training data; Unsupervised learning;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.548861