Scatter Component Analysis (SCA)

June 2, 2019 · View on GitHub

MATLAB implementation of Scatter Component Analysis for domain generalization proposed in paper

Ghifary, M., Balduzzi, D., Kleijn, W. B., & Zhang, M. (2017). Scatter component analysis: A unified framework for domain adaptation and domain generalization. IEEE transactions on pattern analysis and machine intelligence, 39(7), 1414-1430.

Getting Started

The code is tested using MATLAB R2017b on Windows 10. Any later version should work normally.

In MATLAB, change your current folder to "SCA" and run one of the file demo.m to see whether it could run normally.

The file demo.m does the following:

Load synthetic data from "./syn_data/data.m";
Prepare source sample sets (put sample sets 1, 2 in a MATLAB cell array), validation set (sample sets 3, 4 in a matrix), and test set (sample set 5 in a matrix);
Learn transformations using SCA on the source sample sets and validate hyperparameters on the validation set.
Apply the optimal transformation on the test set.

Change your current folder to "SCA" and use the following commands

[test_accuracy, predicted_labels, Zs, Zt] = SCA(X_s_cell, Y_s_cell, X_t, Y_t, params)

Input	Description
X_s_cell	cell of (n_s*d) matrix, each matrix corresponds to the instance features of a source domain
Y_s_cell	cell of (n_s*1) matrix, each matrix corresponds to the instance labels of a source domain
X_t	(n_t*d) matrix, rows correspond to instances and columns correspond to features
Y_t	(n_t*1) matrix, each row is the class label of corresponding instances in X_t
params	optional parameters, details can be found in SCA.m

Output	Description
test_accuracy	test accuracy on target instances
predicted_labels	predicted labels of target instances
Zs	projected source domain instances
Zt	projected target domain instances

See also the list of contributors who participated in this project.

This project is licensed under the MIT License - see the LICENSE file for details.