Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Feature Extraction Based on ICA for Binary Classification Problems
IEEE Transactions on Knowledge and Data Engineering
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Online Learning in High Dimensions
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Knowledge and Information Systems
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality reduction based on ICA for regression problems
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel discriminant analysis for regression problems
Pattern Recognition
Generalization of linear discriminant analysis using Lp-norm
Pattern Recognition Letters
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In this paper, we propose a couple of new feature extraction methods for regression problems. The first one is closely related to the conventional principle component analysis (PCA) but unlike PCA, it incorporates target information in the optimization process and try to find a set of linear transforms that maximizes the distances between points with large differences in target values. On the other hand, the second one is a regressional version of linear discriminant analysis (LDA) which is very popular for classification problems. We have applied the proposed methods to several regression problems and compared the performance with the conventional feature extraction methods. The experimental results show that the proposed methods, especially the extension of LDA, perform well in many regression problems.