Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A statistical framework for genomic data fusion
Bioinformatics
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
An introduction to nonlinear dimensionality reduction by maximum variance unfolding
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Modeling human locomotion with topologically constrained latent variable models
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Localized Multiple Kernel Regression
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Learning a Joint Manifold Representation from Multiple Data Sets
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this work, we analyze video data by learning both the spatial and temporal relationships among frames. For this purpose, the nonlinear dimensionality reduction algorithm, Laplacian Eigenmaps, is improved using a multiple kernel learning framework, and it is assumed that the data can be modeled by means of two different graphs: one considering the spatial information (i.e., the pixel intensity similarities) and the other one based on the frame temporal order. In addition, a formulation for automatic tuning of the required free parameters is stated, which is based on a tradeoff between the contribution of each information source (spatial and temporal). Moreover, we proposed a scheme to compute a common representation in a low-dimensional space for data lying in several manifolds, such as multiple videos of similar behaviors. The proposed algorithm is tested on real-world datasets, and the obtained results allow us to confirm visually the quality of the attained embedding. Accordingly, discussed approach is suitable for data representability when considering cyclic movements.