paper Summaries: Difference between revisions
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==Probabilistic Matrix Factorization== | ==Probabilistic Matrix Factorization== | ||
[[Probabilistic Matrix Factorization]] | [[Probabilistic Matrix Factorization]] | ||
==Probabilistic PCA with Gaussian Process Latent Variable Models== | |||
[[Probabilistic PCA with GPLVM|Probabilistic Principle Component Analysis with Gaussian Process Latent Variable Models]] | |||
Revision as of 00:07, 28 November 2010
Set A
A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
A Direct Formulation For Sparse PCA Using Semidefinite Programming
A Direct Formulation For Sparse PCA Using Semidefinite Programming
Compressive Sensing
Deflation Methods for Sparse PCA
Deflation Methods for Sparse PCA
Supervised Dictionary Learning
Supervised Dictionary Learning
Matrix Completion with Noise
Self-Taught_Learning
Uncovering Shared Structures in Multiclass Classification
A Rank Minimization Heuristic with Application to Minimum Order System Approximation
A Rank Minimization Heuristic with Application to Minimum Order System Approximation
Compressive Sensing (Candes)
Compressive Sensing by Candes et al.
Set B
Multi-Task Feature Learning
Probabilistic Matrix Factorization
Probabilistic Matrix Factorization
Probabilistic PCA with Gaussian Process Latent Variable Models
Probabilistic Principle Component Analysis with Gaussian Process Latent Variable Models