A Multistage Framework With Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain...

Area of Science:

• Machine Learning
• Computer Science

Background:

• Online Unsupervised Domain Adaptation (OUDA) presents challenges with unlabeled, sequential target data.
• Traditional methods often overlook temporal dependencies in arriving data.

Purpose of the Study:

• To propose a novel multi-stage framework for OUDA addressing real-world sequential data challenges.
• To develop a method that considers temporal coherency and accumulative statistics of target data.

Main Methods:

• Introduced Incremental Computation of Mean-Subspace (ICMS) for real-time subspace projection.
• Utilized a recursive-feedback stage with a transformation matrix for domain alignment.
• Leveraged cumulative temporal dependency on Grassmann manifold for subspace prediction.

Main Results:

• The framework demonstrated superior performance over existing approaches in classification accuracy and computational speed.
• Extensive experiments on six datasets validated the contribution of each framework stage.
• The proposed method showed applicability across traditional manifold-based and neural-network-based learning models.

Conclusions:

• The novel framework effectively handles online unsupervised domain adaptation with sequential, unlabeled data.
• ICMS technique and recursive-feedback stage significantly enhance adaptation performance.
• The approach is versatile and applicable to diverse machine learning models.