A Multi-stage Framework with Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation. (arXiv:2207.00003v1 [cs.LG])

In this paper, we address the Online Unsupervised Domain Adaptation (OUDA)
problem and propose a novel multi-stage framework to solve real-world
situations when the target data are unlabeled and arriving online sequentially
in batches. To project the data from the source and the target domains to a
common subspace and manipulate the projected data in real-time, our proposed
framework institutes a novel method, called an Incremental Computation of
Mean-Subspace (ICMS) technique, which computes an approximation of mean-target
subspace on a Grassmann manifold and is proven to be a close approximate to the
Karcher mean. Furthermore, the transformation matrix computed from the
mean-target subspace is applied to the next target data in the
recursive-feedback stage, aligning the target data closer to the source domain.
The computation of transformation matrix and the prediction of next-target
subspace leverage the performance of the recursive-feedback stage by
considering the cumulative temporal dependency among the flow of the target
subspace on the Grassmann manifold. The labels of the transformed target data
are predicted by the pre-trained source classifier, then the classifier is
updated by the transformed data and predicted labels. Extensive experiments on
six datasets were conducted to investigate in depth the effect and contribution
of each stage in our proposed framework and its performance over previous
approaches in terms of classification accuracy and computational speed. In
addition, the experiments on traditional manifold-based learning models and
neural-network-based learning models demonstrated the applicability of our
proposed framework for various types of learning models.

Source: https://arxiv.org/abs/2207.00003


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