PMID- 26766374
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20160701
LR  - 20160630
IS  - 1941-0042 (Electronic)
IS  - 1057-7149 (Linking)
VI  - 25
IP  - 3
DP  - 2016 Mar
TI  - LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation.
PG  - 1177-91
AB  - We propose a novel reconstruction-based transfer learning method called latent 
      sparse domain transfer (LSDT) for domain adaptation and visual categorization of 
      heterogeneous data. For handling cross-domain distribution mismatch, we advocate 
      reconstructing the target domain data with the combined source and target domain 
      data points based on l1-norm sparse coding. Furthermore, we propose a joint 
      learning model for simultaneous optimization of the sparse coding and the optimal 
      subspace representation. In addition, we generalize the proposed LSDT model into 
      a kernel-based linear/nonlinear basis transformation learning framework for 
      tackling nonlinear subspace shifts in reproduced kernel Hilbert space. The 
      proposed methods have three advantages: 1) the latent space and the 
      reconstruction are jointly learned for pursuit of an optimal subspace transfer; 
      2) with the theory of sparse subspace clustering, a few valuable source and 
      target data points are formulated to reconstruct the target data with noise 
      (outliers) from source domain removed during domain adaptation, such that the 
      robustness is guaranteed; and 3) a nonlinear projection of some latent space with 
      kernel is easily generalized for dealing with highly nonlinear domain shift 
      (e.g., face poses). Extensive experiments on several benchmark vision data sets 
      demonstrate that the proposed approaches outperform other state-of-the-art 
      representation-based domain adaptation methods.
FAU - Zhang, Lei
AU  - Zhang L
FAU - Zuo, Wangmeng
AU  - Zuo W
FAU - Zhang, David
AU  - Zhang D
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
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- 2016/01/15 06:00
MHDA- 2016/01/15 06:01
CRDT- 2016/01/15 06:00
PHST- 2016/01/15 06:00 [entrez]
PHST- 2016/01/15 06:00 [pubmed]
PHST- 2016/01/15 06:01 [medline]
AID - 10.1109/TIP.2016.2516952 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 2016 Mar;25(3):1177-91. doi: 10.1109/TIP.2016.2516952.