Joint Distribution Adaptation

This is the implementation of Joint Distribution Adaptation (JDA) in Python and Matlab.
Remark: The core of JDA is a generalized eigendecompsition problem. In Matlab, it can be solved by calling eigs() function. In Python, the implementation scipy.linalg.eig() function can do the same thing. However, they are a bit different. So the results may be different.
The test dataset can be downloaded HERE.
The python file can be used directly, while the matlab code just contains the core function of TCA. To use the matlab code, you need to learn from the code of BDA and set out the parameters.
Reference
Long M, Wang J, Ding G, et al. Transfer feature learning with joint distribution adaptation[C]//Proceedings of the IEEE international conference on computer vision. 2013: 2200-2207.