Unsupervised Domain Adaptation with Random Walks on Target Labelings

This is the implementation of the RWA method for domain adaptation, plus accompanying code to test it on a variety of datasets.

The method itself is implemented in src/rwa/predict_rwa.m. Example:

addpath src/rwa
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_rwa(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

Unsupervised Domain Adaptation with Random Walks on Target Labelings
Twan van Laarhoven, Elena Marchiori
2018

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 RWA method and evaluation code is provided under the MIT license (see file LICENSE). The comparison methods may be subject to other licenses.