On the utility of canonical correlation analysis for domain adaptation in multi-view headpose estimation
2015 IEEE International Conference on Image Processing (ICIP), 2015•ieeexplore.ieee.org
The utility of canonical correlation analysis (CCA) for domain adaptation (DA) in the context
of multi-view head pose estimation is examined in this work. We consider the three problems
studied in [1], where different DA approaches are explored to transfer head pose-related
knowledge from an extensively labeled source dataset to a sparsely labeled target set,
whose attributes are vastly different from the source. CCA is found to benefit DA for all the
three problems, and the use of a covariance profile-based diagonality score (DS) also …
of multi-view head pose estimation is examined in this work. We consider the three problems
studied in [1], where different DA approaches are explored to transfer head pose-related
knowledge from an extensively labeled source dataset to a sparsely labeled target set,
whose attributes are vastly different from the source. CCA is found to benefit DA for all the
three problems, and the use of a covariance profile-based diagonality score (DS) also …
The utility of canonical correlation analysis (CCA) for domain adaptation (DA) in the context of multi-view head pose estimation is examined in this work. We consider the three problems studied in [1], where different DA approaches are explored to transfer head pose-related knowledge from an extensively labeled source dataset to a sparsely labeled target set, whose attributes are vastly different from the source. CCA is found to benefit DA for all the three problems, and the use of a covariance profile-based diagonality score (DS) also improves classification performance with respect to a nearest neighbor (NN) classifier.
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