Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems
ACM Transactions on Mathematical Software (TOMS)
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
IEEE Transactions on Knowledge and Data Engineering
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image retrieval by using tree-structured features and multi-layer self-organizing map
Pattern Analysis & Applications
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Graph-Based Active Learning Based on Label Propagation
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Linear Neighborhood Propagation and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A general graph-based semi-supervised learning with novel class discovery
Neural Computing and Applications
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
Regularized Discriminant Analysis, Ridge Regression and Beyond
The Journal of Machine Learning Research
Semi-supervised kernel canonical correlation analysis with application to human fMRI
Pattern Recognition Letters
On the Theoretical and Computational Analysis between SDA and Lap-LDA
ICTAI '12 Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence - Volume 01
IEEE Transactions on Knowledge and Data Engineering
Hi-index | 0.00 |
Dealing with high-dimensional data has always been a major problem in the research of pattern recognition and machine learning. Among all the dimensionality reduction techniques, Linear Discriminant Analysis (LDA) is one of the most popular methods that have been widely used in many classification applications. But LDA can only utilize labeled samples while neglect the unlabeled samples, which are abundant and can be easily obtained in the real world. In this paper, we propose a new dimensionality reduction method by using unlabeled samples to enhance the performance of LDA. The new method first propagates the label information from labeled set to unlabeled set via a label propagation process, where the predicted labels of unlabeled samples, called soft labels, can be obtained. It then incorporates the soft labels into the construction of scatter matrixes to find a transformed matrix for dimensionality reduction. In this way, the proposed method can preserve more discriminative information, which is preferable when solving the classification problem. Extensive simulations are conducted on several datasets and the results show the effectiveness of the proposed method.