Unsupervised Dataset Dictionary Learning for domain shift robust clustering: application to sitting posture identification
This work addresses domain shift issues for robust clustering in sitting posture identification, but it appears incremental as it builds on existing methods for distribution alignment.
The paper tackles the problem of domain shift in unsupervised clustering for sitting posture identification by introducing U-DaDiL, which uses Wasserstein barycenter-based representation to align distributions, resulting in significant improvements in cluster alignment accuracy on the Office31 dataset.
This paper introduces a novel approach, Unsupervised Dataset Dictionary Learning (U-DaDiL), for totally unsupervised robust clustering applied to sitting posture identification. Traditional methods often lack adaptability to diverse datasets and suffer from domain shift issues. U-DaDiL addresses these challenges by aligning distributions from different datasets using Wasserstein barycenter based representation. Experimental evaluations on the Office31 dataset demonstrate significant improvements in cluster alignment accuracy. This work also presents a promising step for addressing domain shift and robust clustering for unsupervised sitting posture identification