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Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization
A new robust feature selection method with emphasizing joint l2,1-norm minimization on both loss function and regularization is proposed, which has been applied into both genomic and proteomic biomarkers discovery.
The Constrained Laplacian Rank Algorithm for Graph-Based Clustering
This work develops two versions of the Constrained Laplacian Rank (CLR) method, based upon the L1-norm and the L2-norm, which yield two new graph-based clustering objectives and derives optimization algorithms to solve them.
Clustering and projected clustering with adaptive neighbors
This paper proposes a novel clustering model to learn the data similarity matrix and clustering structure simultaneously and derives an efficient algorithm to optimize the proposed challenging problem, and shows the theoretical analysis on the connections between the method and the K-means clustering, and spectral clustering.
Multi-View K-Means Clustering on Big Data
This paper proposes a new robust large-scale multi-view clustering method to integrate heterogeneous representations of largescale data and evaluates the proposed new methods by six benchmark data sets and compared the performance with several commonly used clustering approaches as well as the baseline multi- view clustering methods.
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
- F. Nie, Dong Xu, I. Tsang, Changshui Zhang
- Computer ScienceIEEE Transactions on Image Processing
- 1 July 2010
A unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points by modeling the mismatch between h(X) and F.
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
- Shiming Xiang, F. Nie, Gaofeng Meng, Chunhong Pan, Changshui Zhang
- Computer ScienceIEEE Transactions on Neural Networks and Learning…
- 11 September 2012
The core idea is to enlarge the distance between different classes under the conceptual framework of LSR, and a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged.
Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification
This paper proposes a novel framework via the reformulation of the standard spectral learning model, which can be used for multiview clustering and semisupervised tasks and achieves comparable performance with the state-of-the-art approaches.
Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours
This paper proposes a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously and can allocate ideal weight for each view automatically without additional weight and penalty parameters.
Large-Scale Multi-View Spectral Clustering via Bipartite Graph
A novel large-scale multi-view spectral clustering approach based on the bipartite graph that uses local manifold fusion to integrate heterogeneous features and can be easily extended to handle the out-of-sample problem.
Multi-view Subspace Clustering
- Hongchang Gao, F. Nie, Xuelong Li, Heng Huang
- Computer ScienceIEEE International Conference on Computer Vision…
- 7 December 2015
A novel multi-view subspace clustering method that performs clustering on the subspace representation of each view simultaneously and proposes to use a common cluster structure to guarantee the consistence among different views.