Neural Computation
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
A discriminant analysis for undersampled data
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Clustering Via Local Regression
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Sparsity preserving projections with applications to face recognition
Pattern Recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Supervised learning of local projection kernels
Neurocomputing
Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction
IEEE Transactions on Image Processing
Multiview Metric Learning with Global Consistency and Local Smoothness
ACM Transactions on Intelligent Systems and Technology (TIST)
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Dimensionality reduction with adaptive graph
Frontiers of Computer Science: Selected Publications from Chinese Universities
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This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the projection that has the minimal global estimation error. Then we propose a dimensionality reduction algorithm that leads to the projection with the minimal local estimation error, and elucidate its advantages for classification tasks. We also indicate that LLP keeps the local information in the sense that the projection value of each point can be well estimated based on its neighbors and their projection values. Experimental results are provided to validate the effectiveness of the proposed algorithm.