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Learning from local and global discriminative information for semi-supervised dimensionality reduction.

, , and . IJCNN, page 1-8. IEEE, (2013)

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Semi-Supervised Image Classification by Nonnegative Sparse Neighborhood Propagation., , , , , and . ICMR, page 139-146. ACM, (2015)Discriminative sparse flexible manifold embedding with novel graph for robust visual representation and label propagation., , , , , and . Pattern Recognit., (2017)M-Isomap: Orthogonal Constrained Marginal Isomap for Nonlinear Dimensionality Reduction., , and . IEEE Trans. Cybern., 43 (1): 180-191 (2013)Binary- and Multi-class Group Sparse Canonical Correlation Analysis for Feature Extraction and Classification., , and . IEEE Trans. Knowl. Data Eng., 25 (10): 2192-2205 (2013)Label propagation and soft-similarity measure for graph based Constrained Semi-Supervised Learning., , and . IJCNN, page 2927-2934. IEEE, (2014)Locality Constrained-ℓp Sparse Subspace Clustering for Image Clustering., , and . Neurocomputing, (2016)Projective Label Propagation by Label Embedding., , , , , and . CAIP (2), volume 9257 of Lecture Notes in Computer Science, page 470-481. Springer, (2015)Transformed Neighborhood Propagation., , and . ICPR, page 3792-3797. IEEE Computer Society, (2014)On the Theoretical and Computational Analysis between SDA and Lap-LDA., , and . ICTAI, page 122-129. IEEE Computer Society, (2012)Weighted local and global regressive mapping: A new manifold learning method for machine fault classification., , , and . Eng. Appl. Artif. Intell., (2014)