Uncorrelated feature selection via sparse latent representation and extended OLSDA

Highlights：

• SLREO performs feature selection in the latent representation space, uses latent representation learning to mine the hidden information between data, and retains the interconnection between data.

• SLREO generates pseudo-label information through the OLSDA method embedded in a non-negative manifold structure.

• The l2,1-norm constraint is imposed on the residual matrix of latent representation learning to ensure the robustness of the clustering indicators.

• Unifying the latent representation matrix and the clustering index matrix can preserve the dependencies between data and ensure non-negative of pseudo-labels.

• Applying uncorrelated constraint and l2,1-norm constraint on the feature transformation matrix can avoid excessive suppression of non-zero rows and the appearance of redundant solutions. And more discriminative features can be selected.