paper Summaries: Difference between revisions
Jump to navigation
Jump to search
(→Set B) |
No edit summary |
||
| Line 32: | Line 32: | ||
==Probabilistic PCA with Gaussian Process Latent Variable Models== | ==Probabilistic PCA with Gaussian Process Latent Variable Models== | ||
[[Probabilistic PCA with GPLVM|Probabilistic Principle Component Analysis with Gaussian Process Latent Variable Models]] | [[Probabilistic PCA with GPLVM|Probabilistic Principle Component Analysis with Gaussian Process Latent Variable Models]] | ||
==Consistency of Trace Norm Minimization== | |||
[Consistency of Trace Norm Minimization] | |||
Revision as of 22:52, 1 December 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
Consistency of Trace Norm Minimization
[Consistency of Trace Norm Minimization]