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Dimensionality Reduction

706 directly classified papers

Papers per year

Papers

Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning NIPS 2012
Semiparametric Principal Component Analysis NIPS 2012
On the Sample Complexity of Robust PCA NIPS 2012
Factorial LDA: Sparse Multi-Dimensional Text Models NIPS 2012
Symmetric Correspondence Topic Models for Multilingual Text Analysis NIPS 2012
Perfect Dimensionality Recovery by Variational Bayesian PCA NIPS 2012
Learning Anchor Planes for Classification NIPS 2011
Divide-and-Conquer Matrix Factorization NIPS 2011
Multi-View Learning of Word Embeddings via CCA NIPS 2011
Dimensionality Reduction for Spectral Clustering AISTATS 2011
CAKE: Convex Adaptive Kernel Density Estimation AISTATS 2011
Discussion of “Spectral Dimensionality Reduction via Maximum Entropy” AISTATS 2011
Spectral Dimensionality Reduction via Maximum Entropy AISTATS 2011
Sparse Manifold Clustering and Embedding NIPS 2011
Demixed Principal Component Analysis NIPS 2011
Structural equations and divisive normalization for energy-dependent component analysis NIPS 2011
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators NIPS 2011
Efficient anomaly detection using bipartite k-NN graphs NIPS 2011
Data Skeletonization via Reeb Graphs NIPS 2011
Learning low-rank output kernels ACML 2011
Mixed-Variate Restricted Boltzmann Machines ACML 2011
Unsupervised Multiple Kernel Learning ACML 2011
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data NIPS 2011
Large-Scale Sparse Principal Component Analysis with Application to Text Data NIPS 2011
k-NN Regression Adapts to Local Intrinsic Dimension NIPS 2011