IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Component-Based Face Recognition with 3D Morphable Models
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
A least squares formulation for canonical correlation analysis
Proceedings of the 25th international conference on Machine learning
Semi-supervised sub-manifold discriminant analysis
Pattern Recognition Letters
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
IEEE Transactions on Neural Networks
Semi-supervised metric learning using pairwise constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
A scalable two-stage approach for a class of dimensionality reduction techniques
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised learning based on high density region estimation
Neural Networks
LPP solution schemes for use with face recognition
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and Beyond
Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and Beyond
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Active selection of clustering constraints: a sequential approach
Pattern Recognition
Expert Systems with Applications: An International Journal
Soft label based Linear Discriminant Analysis for image recognition and retrieval
Computer Vision and Image Understanding
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In this work, sub-manifold projections based semi-supervised dimensionality reduction (DR) problem learning from partial constrained data is discussed. Two semi-supervised DR algorithms termed Marginal Semi-Supervised Sub-Manifold Projections (MS^3MP) and orthogonal MS^3MP (OMS^3MP) are proposed. MS^3MP in the singular case is also discussed. We also present the weighted least squares view of MS^3MP. Based on specifying the types of neighborhoods with pairwise constraints (PC) and the defined manifold scatters, our methods can preserve the local properties of all points and discriminant structures embedded in the localized PC. The sub-manifolds of different classes can also be separated. In PC guided methods, exploring and selecting the informative constraints is challenging and random constraint subsets significantly affect the performance of algorithms. This paper also introduces an effective technique to select the informative constraints for DR with consistent constraints. The analytic form of the projection axes can be obtained by eigen-decomposition. The connections between this work and other related work are also elaborated. The validity of the proposed constraint selection approach and DR algorithms are evaluated by benchmark problems. Extensive simulations show that our algorithms can deliver promising results over some widely used state-of-the-art semi-supervised DR techniques.