Some Notes on Alternating Optimization
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
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)
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Biased discriminant euclidean embedding for content-based image retrieval
IEEE Transactions on Image Processing
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Non-goal scene analysis for soccer video
Neurocomputing
m-SNE: multiview stochastic neighbor embedding
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Feature Fusion Using Multiple Component Analysis
Neural Processing Letters
Difficulty guided image retrieval using linear multiview embedding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Content-adaptive reliable robust lossless data embedding
Neurocomputing
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
Fast multi-view graph kernels for object classification
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Finding suits in images of people
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
RGB-D based multi-attribute people search in intelligent visual surveillance
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Discriminative information preservation for face recognition
Neurocomputing
Joint geometry and variability for image recognition
Neurocomputing
Transductive cartoon retrieval by multiple hypergraph learning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Fast multi-view segment graph kernel for object classification
Signal Processing
Global Similarity in Social Networks with Typed Edges
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Towards information-theoretic K-means clustering for image indexing
Signal Processing
Local 3d symmetry for visual saliency in 2.5d point clouds
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Web page and image semi-supervised classification with heterogeneous information fusion
Journal of Information Science
Multi-view hypergraph learning by patch alignment framework
Neurocomputing
Multiview Hessian discriminative sparse coding for image annotation
Computer Vision and Image Understanding
Quality of information-based source assessment and selection
Neurocomputing
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In computer vision and multimedia search, it is common to use multiple features from different views to represent an object. For example, to well characterize a natural scene image, it is essential to find a set of visual features to represent its color, texture, and shape information and encode each feature into a vector. Therefore, we have a set of vectors in different spaces to represent the image. Conventional spectral-embedding algorithms cannot deal with such datum directly, so we have to concatenate these vectors together as a new vector. This concatenation is not physically meaningful because each feature has a specific statistical property. Therefore, we develop a new spectral-embedding algorithm, namely, multiview spectral embedding (MSE), which can encode different features in different ways, to achieve a physically meaningful embedding. In particular, MSE finds a low-dimensional embedding wherein the distribution of each view is sufficiently smooth, and MSE explores the complementary property of different views. Because there is no closed-form solution for MSE, we derive an alternating optimization-based iterative algorithm to obtain the low-dimensional embedding. Empirical evaluations based on the applications of image retrieval, video annotation, and document clustering demonstrate the effectiveness of the proposed approach.