Abstract
Domain adaptation (DA) is a method used to obtain better classification accuracy, when the training and testing datasets have different distributions. This paper describes an algorithm for DA to transform data from source domain to match the distribution of the target domain. We use eigen-analysis of data on both the domains, to estimate the transformation along each dimension separately. In order to parameterize the distributions in both the domains, we perform clustering separately along every dimension, prior to the transformation. The proposed algorithm of DA when applied to the task of object categorization, gives better results than a few state of the art methods.
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Samanta, S., Das, S. (2013). Domain Adaptation Based on Eigen-Analysis and Clustering, for Object Categorization. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_29
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DOI: https://doi.org/10.1007/978-3-642-40261-6_29
Publisher Name: Springer, Berlin, Heidelberg
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