Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Convergence of alternating optimization
Neural, Parallel & Scientific Computations
Convex Optimization
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Annotating personal albums via web mining
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Clustering with Local and Global Regularization
IEEE Transactions on Knowledge and Data Engineering
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Parallel Spectral Clustering in Distributed Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speed up kernel discriminant analysis
The VLDB Journal — The International Journal on Very Large Data Bases
Locally Consistent Concept Factorization for Document Clustering
IEEE Transactions on Knowledge and Data Engineering
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Regularized Gaussian Mixture Model for Data Clustering
IEEE Transactions on Knowledge and Data Engineering
Agglomerative Mean-Shift Clustering
IEEE Transactions on Knowledge and Data Engineering
Locally Discriminative Coclustering
IEEE Transactions on Knowledge and Data Engineering
Manifold Adaptive Experimental Design for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Mirror descent and nonlinear projected subgradient methods for convex optimization
Operations Research Letters
Spectral Clustering on Multiple Manifolds
IEEE Transactions on Neural Networks
Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering
IEEE Transactions on Neural Networks
Robust Principal Component Analysis Based on Maximum Correntropy Criterion
IEEE Transactions on Image Processing
Relational co-clustering via manifold ensemble learning
Proceedings of the 21st ACM international conference on Information and knowledge management
Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation
Expert Systems with Applications: An International Journal
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A large number of data are generated in many real-world applications, e.g., photos of albums in social networks. Discovering meaningful patterns from them is desirable and still remains a big challenge. To this end, spectral clustering has established itself as a very useful tool for data analysis. It considers the manifold geometrical structure of the data and desires to estimate the intrinsic manifold. However, there exists no principled way for estimating such manifold. Thus, the clustering performance might even degrade seriously when the estimated manifold deviates far from the intrinsic manifold. To address this problem, we propose to employ composite manifold to approximate the intrinsic manifold as much as possible. This composite manifold is derived from a convex combination of some pre-given candidate manifolds in a convex hull. The diversity nature of these manifolds provides richer structure information, which is helpful to maximally estimate the intrinsic manifold. Besides, traditional spectral clustering neglects the discriminant information latent in the data space, so we incorporate the locally discriminative structure into the partition matrix by explicitly using local linear regression, for better clustering. Therefore, in this paper, we present an integrated clustering approach named Spectral Clustering via Composite manifold and Local discriminant learning, referred to as SCCL for short, which addresses the aforementioned two problems. To optimize the objective function, an alternating optimization framework is adopted to derive both the cluster membership matrix and the composite manifold coefficient. Extensive experiments are carried out on several real-world databases. Results have validated the efficacy of the proposed algorithm.