调研记录
Feiping Nie and Shiming Xiang(20130903)，将他们的论文2009-2013的标题都看了，不必再调研。最新的论文分别是 Efficient Image Classification via Multiple Rank Regression
和Nonparametric Illumination Correction

have  seen, no need to see again
Robust Classification via Structured Sparse Representation (CVPR 2011)
Patch alignment 

need to see:
非传统人脸识别
Coupled Discriminant Analysis for Heterogeneous Face Recognition
Discriminative Multimanifold Analysis for Face Recognition from a Single Training Sample per Person (TPAMI 2013 Feature article)

A General Iterative Shrinkage and Thresholding Algorithm for(ICML 13,有code)
Similarity Component Analysis
Unsupervised and Semi-Supervised Learning via ℓ1-Norm Graph (Feiping Nie,有code)
Local Structure-based Image Decomposition for Feature Extraction with Applications to Face Recognition （TIP）
Sparse representation classifier steered discriminant projections (TNNLS 2013)
Tumor Classification Based on Non-Negative Matrix(chunhou zheng)
L-2,1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning(IJCAI,有code)
Towards structural sparsity An explicit l2 l0 approach (主要看下该文Lipchitz辅助函数怎么用的，Chris Ding的讲稿"sparseBeijing_Christ Ding"第28页ppt提到了)
Manifold Adaptive Experimental Design for Text Categorization Deng Cai(TKDE 2012,有code)
Sparse concept coding for visual analysis (CVPR 2011,有code)
A nove lSVM+NDA （Pattern recognition）
ICDM 2010 L2/L0-norm,包括Chris Ding的讲稿
(2011) R. Jenatton, J.-Y. Audibert and F. Bach. Structured Variable Selection with Sparsity-Inducing Norms. Journal of Machine Learning Research, 12(Oct):2777-2824.  (20120312开始的一周  和libing 计划将这篇论文看完)
Robust Sparse Coding for Face Recognition (discuss with libing, he said he has understood totally)
Feature selection
Linear Discriminant Dimensionality Reduction(ECML 2011)
Generalized Fisher Score for Feature Selection(UAI 2011)

有空再看的论文：
Extreme Learning Machine for Regression and Multiclass Classification（TSMCB 2012）

gains：
1、know how to derive formula (13) in SRC (Sparse representation classifier(稀疏表示分类器), Yi Ma, TPAMI 2009); know how to derive formula (14) in “Efficient and Robust Feature Selection via Joint L2,1-Norms Minimization (NIPS 2010)”. The key points are formulas (16) and (17).
2、know how to derive from formulas (10) to (12) in "R1-PCA Rotational invariant L1-norm principal component analysis for robust subspace factorization (ICML 2006)"