Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Semi-supervised nonlinear dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust path-based spectral clustering
Pattern Recognition
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
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Curse of dimensionality is a bothering problem in high dimensional data analysis. To enhance the performances of classification or clustering on these data, their dimensionalities should be reduced beforehand. Locality Preserving Projections (LPP) is a widely used linear dimensionality reduction method. It seeks a subspace in which the neighborhood graph structure of samples is preserved. However, like most dimensionality reduction methods based on graph embedding, LPP is sensitive to noise and outliers, and its effectiveness depends on choosing suitable parameters for constructing the neighborhood graph. Unfortunately, it is difficult to choose effective parameters for LPP. To address these problems, we propose an Enhanced LPP (ELPP) using a similarity metric based on robust path and a Semi-supervised ELPP (SELPP) with pairwise constraints. In comparison with original LPP, our methods are not only robust to noise and outliers, but also less sensitive to parameters selection. Besides, SELPP makes use of pairwise constraints more efficiently than other comparing methods. Experimental results on real world face databases confirm their effectiveness.