paper Summaries: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
|||
| Line 1: | Line 1: | ||
=Set A= | |||
==A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis== | ==A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis== | ||
[[A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis]] | [[A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis]] | ||
| Line 15: | Line 16: | ||
==Self-Taught_Learning== | ==Self-Taught_Learning== | ||
[[Self-Taught Learning]] | [[Self-Taught Learning]] | ||
==Uncovering Shared Structures in Multiclass Classification== | ==Uncovering Shared Structures in Multiclass Classification== | ||
[[Uncovering Shared Structures in Multiclass Classification]] | [[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== | ||
[[A Rank Minimization Heuristic with Application to Minimum Order System Approximation]] | [[A Rank Minimization Heuristic with Application to Minimum Order System Approximation]] | ||
=Set B= | |||
==Multi-Task Feature Learning== | ==Multi-Task Feature Learning== | ||
[[Multi-Task Feature Learning]] | [[Multi-Task Feature Learning]] | ||
Revision as of 19:42, 24 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