GTM: the generative topographic mapping
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
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
Compressed sensing and Bayesian experimental design
Proceedings of the 25th international conference on Machine learning
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Convex multi-task feature learning
Machine Learning
IEEE Transactions on Signal Processing
Hash Kernels for Structured Data
The Journal of Machine Learning Research
Semi-supervised local fisher discriminant analysis for dimensionality reduction
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Kernel laplacian eigenmaps for visualization of non-vectorial data
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
IEEE Transactions on Signal Processing
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Stability of dimensionality reduction methods applied on artificial hyperspectral images
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Bilinear discriminative dictionary learning for face recognition
Pattern Recognition
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Compressive sensing is an emerging field predicated upon the fact that, if a signal has a sparse representation in some basis, then it can be almost exactly reconstructed from very few random measurements. Many signals and natural images, for example under the wavelet basis, have very sparse representations, thus those signals and images can be recovered from a small amount of measurements with very high accuracy. This paper is concerned with the dimensionality reduction problem based on the compressive assumptions. We propose novel unsupervised and semi-supervised dimensionality reduction algorithms by exploiting sparse data representations. The experiments show that the proposed approaches outperform state-of-the-art dimensionality reduction methods.