Zhu JJang-Jaccard JLiu TZhou J2021-022021-02NEURAL PROCESSING LETTERS, 2021, 53 (1), pp. 257 - 2731370-4621https://hdl.handle.net/10179/17427CAUL read and publish agreement 2022Redundant features and outliers (noise) included in the data points for a machine learning clustering model heavily influences the discovery of more distinguished features for clustering. To solve this issue, we propose a spectral new clustering method to consider the feature selection with the L2 , 1-norm regularization as well as simultaneously learns orthogonal representations for each sample to preserve the local structures of data points. Our model also solves the issue of out-of-sample, where the training process does not output an explicit model to predict unseen data points, along with providing an efficient optimization method for the proposed objective function. Experimental results showed that our method on twelve data sets achieves the best performance compared with other similar models.257 - 273Feature selectionClusteringGraph matrixdimensionality reductionsubspace learningJournal article10.1007/s11063-020-10383-94360941573-773XMassey_Dark0801 Artificial Intelligence and Image Processing1702 Cognitive Sciences