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.
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Why is it important?
The proposed method is fast to compute and also robust as it can easily handle non-linear transformation of data.
Perspectives
The work proposed in this paper describes a simple yet effective way to transform instances of the training set to match the distribution of the test set. The method has been applied to a wide range of dataset such as, image, video and text, which shows the effectiveness of the proposed method.
Suranjana Samanta
Indian Institute of Technology Madras
Read the Original
This page is a summary of: Unsupervised domain adaptation using eigenanalysis in kernel space for categorisation tasks, IET Image Processing, November 2015, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-ipr.2014.0754.
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