Domain Adaptation by Eigen Analysis
What is it about?
Domain adaptation is a technique needed for robust machine learning algorithms. It deals with the situation when the training and the testing samples do not follow the same underlying distribution. In this work, we have used the Eigenvectors and Eigenvalues of the training ans the testing sample to find a suitable transformation. The transformed instances of the training samples have identical Eigen analysis results as that of the test samples.
Why is it important?
The proposed method is fast to compute and also robust as it can easily handle non-linear transformation of data.
The following have contributed to this page: Suranjana Samanta
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