PMID- 27046875
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20170519
LR  - 20170519
IS  - 1941-0042 (Electronic)
IS  - 1057-7149 (Linking)
VI  - 25
IP  - 6
DP  - 2016 Jun
TI  - Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust 
      Representation and Visual Classification.
PG  - 2429-43
LID - 10.1109/TIP.2016.2547180 [doi]
AB  - Recovering low-rank and sparse subspaces jointly for enhanced robust 
      representation and classification is discussed. Technically, we first propose a 
      transductive low-rank and sparse principal feature coding (LSPFC) formulation 
      that decomposes given data into a component part that encodes low-rank sparse 
      principal features and a noise-fitting error part. To well handle the outside 
      data, we then present an inductive LSPFC (I-LSPFC). I-LSPFC incorporates embedded 
      low-rank and sparse principal features by a projection into one problem for 
      direct minimization, so that the projection can effectively map both inside and 
      outside data into the underlying subspaces to learn more powerful and informative 
      features for representation. To ensure that the learned features by I-LSPFC are 
      optimal for classification, we further combine the classification error with the 
      feature coding error to form a unified model, discriminative LSPFC (D-LSPFC), to 
      boost performance. The model of D-LSPFC seamlessly integrates feature coding and 
      discriminative classification, so the representation and classification powers 
      can be enhanced. The proposed approaches are more general, and several recent 
      existing low-rank or sparse coding algorithms can be embedded into our problems 
      as special cases. Visual and numerical results demonstrate the effectiveness of 
      our methods for representation and classification.
FAU - Zhang, Zhao
AU  - Zhang Z
FAU - Li, Fanzhang
AU  - Li F
FAU - Zhao, Mingbo
AU  - Zhao M
FAU - Zhang, Li
AU  - Zhang L
FAU - Yan, Shuicheng
AU  - Yan S
LA  - eng
PT  - Journal Article
DEP - 20160325
PL  - United States
TA  - IEEE Trans Image Process
JT  - IEEE transactions on image processing : a publication of the IEEE Signal 
      Processing Society
JID - 9886191
EDAT- 2016/04/06 06:00
MHDA- 2016/04/06 06:01
CRDT- 2016/04/06 06:00
PHST- 2016/04/06 06:00 [entrez]
PHST- 2016/04/06 06:00 [pubmed]
PHST- 2016/04/06 06:01 [medline]
AID - 10.1109/TIP.2016.2547180 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 2016 Jun;25(6):2429-43. doi: 10.1109/TIP.2016.2547180. 
      Epub 2016 Mar 25.