Domain Adaptation with Randomized Expectation Maximization
This is the implementation of the AdREM method for domain adaptation, plus accompanying code to test it on a variety of datasets.
The method itself is implemented in src/adrem/predict_adrem.m.
Example:
addpath src/adrem
addpath src/evaluation
data = load_dataset('amazon');
% Predict labels of 'dvd' target domain using 'books' as source
% Preprocess data: divide by standard deviation over both domains
[x_src, x_tgt] = preprocess(data.x{1}, data.y{1}, data.x{2}, 'joint-std');
y = predict_adrem(x_src, data.y{1}, x_tgt);
mean(y == data.y{2})
The method requires that the MATLAB/octave bindings for liblinear are installed. These can be downloaded from https://www.csie.ntu.edu.tw/~cjlin/liblinear/
As a starting point for the experiments, look at src/evaluation/run_methods.m.
If you use this code, please cite
Biased Bagging for Unsupervised Domain Adaptation
Twan van Laarhoven, Elena Marchiori
2017
Additional information can be found at http://twanvl.nl/research/domain-adaptation-2017/ The datasets used in the experiments can also be downloaded there.
Comparison methods
The comparison methods can be downloaded from:
- GFK: http://www-scf.usc.edu/~boqinggo/domainadaptation.html
- FLDA: https://github.com/wmkouw/da-fl
- Domain adaptation toolbox: https://github.com/viggin/domain-adaptation-toolbox
- Coral: included in the source code
- Subspace Alignment: included in the source code
License
The AdREM method and evaluation code is provided under the MIT license (see file LICENSE). The comparison methods may be subject to other licenses.