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Maximum independence domain adaptation (MIDA)

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A feaure-level transfer learning (domain adaptation) algorithm



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A domain-invariant subspace will be learned. MIDA can be applied in all kinds of domain adaptation problems, including discrete or continuous distributional change, supervised/semi-supervised/unsupervised, multiple domains, classification or regression, etc. All domains can be unlabeled/labeled/partially labeled. Suitable for transfer learning, domain adaptation, and concept drift adaptation (e.g. sensor drift correction) problems. Two test cases are in testMida.m.
ref: Ke Yan, Lu Kou, and David Zhang, "Domain Adaptation via Maximum Independence of Domain Features,"
Copyright 2016 YAN Ke, Tsinghua Univ. ,

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