paper Summaries

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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

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

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 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 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