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Maximum independence domain adaptation (MIDA)

version 1.1.0.0 (32.2 KB) by Ke Yan

Ke Yan (view profile)

A feaure-level transfer learning (domain adaptation) algorithm

5 Downloads

Updated 20 Apr 2016

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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," http://arxiv.org/abs/1603.04535
Copyright 2016 YAN Ke, Tsinghua Univ. http://yanke23.com , xjed09@gmail.com

Comments and Ratings (1)

Esther Kui

Hello Ke Yan.
Excellent work on this submission.
Could you check if you forgot to put in the sigmoid function's definition in the files class_lr_te and class_lr_tr? I had to add the functions manually because testMIDa would not run.

Updates

1.1.0.0

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MATLAB Release Compatibility
Created with R2011a
Compatible with any release
Platform Compatibility
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