Regularized Semipaired Kernel CCA for Domain Adaptation.

Area of Science:

• Machine Learning
• Pattern Recognition
• Artificial Intelligence

Background:

• Domain adaptation is crucial for applying models to new data distributions.
• Existing methods often struggle with large datasets and out-of-sample generalization.
• Integrating diverse data sources (domains) remains a significant challenge.

Purpose of the Study:

• To develop a robust domain adaptation technique using kernel canonical correlation analysis.
• To learn a joint latent representation across different data domains.
• To improve classification performance on target domains by leveraging source domain data.

Main Methods:

• A regularized semipaired kernel canonical correlation analysis (RKCCA) formulation is proposed.
• The optimization is performed within a primal-dual least squares support vector machine framework.
• Nyström approximation is employed to handle large-scale eigendecomposition problems.
• The learned latent space is utilized by a multiclass semisupervised kernel spectral clustering model.

Main Results:

• The proposed RKCCA method effectively learns a joint representation for domain adaptation.
• The approach demonstrates strong out-of-sample extension capabilities for model selection.
• Computational feasibility is achieved for large datasets via Nyström approximation.
• Experimental results show significant improvements in classifying target domain data.

Conclusions:

• The developed regularized semipaired kernel canonical analysis offers an effective solution for domain adaptation.
• The integration with semisupervised spectral clustering enhances classification accuracy using both labeled and unlabeled data.
• The method provides a scalable and generalizable framework for cross-domain learning problems.