Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification

@article{Samat2017SupervisedAS,
  title={Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification},
  author={Alim Samat and Claudio Persello and Paolo Gamba and Sicong Liu and Jilili Abuduwaili and Erzhu Li},
  journal={Remote Sensing},
  year={2017},
  volume={9},
  pages={337}
}
In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from different heterogeneous domains. Specifically, the multi-view canonical correlation analysis scheme is utilized to extract multiple correlation subspaces that are useful for joint… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 67 references

Logistic label propagation

Pattern Recognition Letters • 2012
View 9 Excerpts
Highly Influenced

Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2013
View 5 Excerpts
Highly Influenced

Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach

IEEE Transactions on Neural Networks and Learning Systems • 2012
View 5 Excerpts
Highly Influenced

Domain adaptation for object recognition: An unsupervised approach

2011 International Conference on Computer Vision • 2011
View 4 Excerpts
Highly Influenced

Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation • 2000
View 3 Excerpts
Highly Influenced

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