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=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 7: | Line 8: | ||
==Compressive Sensing== | ==Compressive Sensing== | ||
[[Compressive Sensing]] | [[Compressive Sensing]] | ||
== | ==Deflation Methods for Sparse PCA== | ||
[[ | [[Deflation Methods for Sparse PCA]] | ||
==Supervised Dictionary Learning== | |||
[[Supervised Dictionary Learning]] | |||
==Matrix Completion with Noise== | |||
[[Matrix Completion with Noise]] | |||
==Self-Taught_Learning== | |||
[[Self-Taught Learning]] | |||
==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]] | |||
==Compressive Sensing (Candes)== | |||
[[Compressive_Sensing_(Candes) | Compressive Sensing by Candes et al.]] | |||
=Set B= | |||
==Multi-Task Feature Learning== | |||
[[Multi-Task Feature Learning]] | |||
==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]] | |||
==Consistency of Trace Norm Minimization== | |||
[[Consistency of Trace Norm Minimization]] | |||
==Optimal Solutions forSparse Principal Component Analysis== | |||
[[Optimal Solutions forSparse Principal Component Analysis]] | |||
==A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization== | |||
[[A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization]] | |||
Latest revision as of 08:45, 30 August 2017
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
Optimal Solutions forSparse Principal Component Analysis
Optimal Solutions forSparse Principal Component Analysis
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization