Question About mv-loss

[ky941122]

Hi, I have recently read your MLCR paper and am very interested in your ideas. But I'm a little confused about the mv-loss.
Why don't you just constrain the features of different views to be mutually orthogonal, but go for the classifier's weights of different views instead?

[nxsEdson]

Sorry for the late reply, and thank you for your interests in our work. There are two reasons for using the weights instead of the features: 1) the features are shared and used for multilabel classification, e.g., 12 AU classification tasks, and 2) the weights for each AU can be regarded as a representation of the features for this specific AU because the weights vector can be seen as the base vector for projection, and the projection results are the final prediction.