Sparse robust adaptive unsupervised subspace learning for dimensionality reduction

This work is devoted to the investigation of dimension reduction problem. As an efficient dimension reduction method, much attention has been paid on unsupervised subspace learning since it does not rely on expensive labels. Firstly, we implant a robust estimator in the error term of objective function, this leads to that small coefficients can be automa L2,-norm (1 <= r <= 2) is used as a measure of error, then, the performance of the model can be improved by selecting the appropriate adaptive parameter r. Further, L-2p-norm (0更多