# paper Summaries

From statwiki

Revision as of 08:45, 30 August 2017 by Conversion script (talk | contribs) (Conversion script moved page Paper Summaries to paper Summaries: Converting page titles to lowercase)

## Contents

- 1 Set A
- 1.1 A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis
- 1.2 DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
- 1.3 A Direct Formulation For Sparse PCA Using Semidefinite Programming
- 1.4 Compressive Sensing
- 1.5 Deflation Methods for Sparse PCA
- 1.6 Supervised Dictionary Learning
- 1.7 Matrix Completion with Noise
- 1.8 Self-Taught_Learning
- 1.9 Uncovering Shared Structures in Multiclass Classification
- 1.10 A Rank Minimization Heuristic with Application to Minimum Order System Approximation
- 1.11 Compressive Sensing (Candes)

- 2 Set B
- 2.1 Multi-Task Feature Learning
- 2.2 Probabilistic Matrix Factorization
- 2.3 Probabilistic PCA with Gaussian Process Latent Variable Models
- 2.4 Consistency of Trace Norm Minimization
- 2.5 Optimal Solutions forSparse Principal Component Analysis
- 2.6 A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

# 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