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 , firstname.lastname@example.org
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.
add a figure