Original paper
Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning
Published: Dec 1, 2016
Abstract
Domain adaptation (DA) algorithms utilize a label-rich old dataset (domain) to build a machine learning model (classification, detection etc.) in a label-scarce new dataset with different data distribution. Recent approaches transform cross-domain data into a shared subspace by minimizing the shift between their marginal distributions. In this paper, we propose a novel iterative method to learn a common subspace based on non-parametric quadratic...
Paper Details
Title
Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning
Published Date
Dec 1, 2016
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