Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Notes on Alternating Optimization
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Adaptive control in cartoon data reusing
Computer Animation and Virtual Worlds - CASA 2007
Optimizing multi-graph learning: towards a unified video annotation scheme
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Perspective-aware cartoon clips synthesis
Computer Animation and Virtual Worlds - CASA'2008 Special Issue
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Biased discriminant euclidean embedding for content-based image retrieval
IEEE Transactions on Image Processing
Cartoon synthesis using constrained spreading activation network
Multimedia Tools and Applications
Enhanced independent component analysis and its application to content based face image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
IEEE Transactions on Image Processing
Subspaces Indexing Model on Grassmann Manifold for Image Search
IEEE Transactions on Image Processing
Complex Object Correspondence Construction in Two-Dimensional Animation
IEEE Transactions on Image Processing
Quality of information-based source assessment and selection
Neurocomputing
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In recent years we witnessed a surge of interest in subspace learning for image classification. However, the previous methods lack of high accuracy since they do not consider multiple features of the images. For instance, we can represent a color image by finding a set of visual features to represent the information of its color, texture and shape. According to the ''Patch Alignment'' Framework, we developed a new subspace learning method, termed Semi-Supervised Multimodal Subspace Learning (SS-MMSL), in which we can encode different features from different modalities to build a meaningful subspace. In particular, the new method adopts the discriminative information from the labeled data to construct local patches and aligns these patches to get the optimal low dimensional subspace for each modality. For local patch construction, the data distribution revealed by unlabeled data is utilized to enhance the subspace learning. In order to find a low dimensional subspace wherein the distribution of each modality is sufficiently smooth, SS-MMSL adopts an alternating and iterative optimization algorithm to explore the complementary characteristics of different modalities. The iterative procedure reaches the global minimum of the criterion due to the strong convexity of the criterion. Our experiments of image classification and cartoon retrieval demonstrate the validity of the proposed method.