Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
An equivalence between sparse approximation and support vector machines
Neural Computation
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Heterogeneous multimedia data semantics mining using content and location context
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
Semi-Supervised Learning
Semi-supervised ensemble classification in subspaces
Applied Soft Computing
Image annotation by semi-supervised cross-domain learning with group sparsity
Journal of Visual Communication and Image Representation
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Linear discriminant analysis (LDA) is a well-known dimensionality reduction method which can be easily extended for data classification. Traditional LDA aims to preserve the separability of different classes and the compactness of the same class in the output space by maximizing the between-class covariance and simultaneously minimizing the within-class covariance. However, the performance of LDA usually deteriorates when labeled information is insufficient. In order to resolve this problem, semi-supervised learning can be used, among which, manifold regularization (MR) provides an elegant framework to learn from labeled and unlabeled data. However, MR tends to misclassify data near the boundaries of different clusters during classification. In this paper, we propose a novel method, referred to as semi-supervised discriminative regularization (SSDR), to incorporate LDA and MR into a coherent framework for data classification, which exploits both label information and data distribution. Extensive experiments demonstrate the effectiveness of our proposed method in comparison with classical classification algorithms including SVM, LDA and MR.