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Dimensionality Reduction

707 papers

Papers per year

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Papers

Locality Preserving Feature Learning AISTATS 2012
Online Clustering of Processes AISTATS 2012
Minimax Rates of Estimation for Sparse PCA in High Dimensions AISTATS 2012
Distance Preserving Embeddings for General n-Dimensional Manifolds COLT 2012
Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries COLT 2012
Algebraic Geometric Comparison of Probability Distributions JMLR 2012
Local and Global Scaling Reduce Hubs in Space JMLR 2012
k-NN Regression Adapts to Local Intrinsic Dimension NIPS 2011
Regularized Laplacian Estimation and Fast Eigenvector Approximation NIPS 2011
Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach NIPS 2011
Large-Scale Sparse Principal Component Analysis with Application to Text Data NIPS 2011
Learning Anchor Planes for Classification NIPS 2011
Dimensionality Reduction Using the Sparse Linear Model NIPS 2011
A Denoising View of Matrix Completion NIPS 2011
Data Skeletonization via Reeb Graphs NIPS 2011
Divide-and-Conquer Matrix Factorization NIPS 2011
Multi-View Learning of Word Embeddings via CCA NIPS 2011
Efficient anomaly detection using bipartite k-NN graphs NIPS 2011
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators NIPS 2011
Structural equations and divisive normalization for energy-dependent component analysis NIPS 2011
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data NIPS 2011
Demixed Principal Component Analysis NIPS 2011
Sparse Manifold Clustering and Embedding NIPS 2011
Unsupervised Multiple Kernel Learning ACML 2011
Learning low-rank output kernels ACML 2011