Kernelization of Tensor-Based Models for Multiway Data Analysis: Processing of Multidimensional Structured Data
暂无分享,去创建一个
Liqing Zhang | Andrzej Cichocki | Tülay Adali | Qibin Zhao | Guoxu Zhou | A. Cichocki | Liqing Zhang | T. Adalı | Guoxu Zhou | Qibin Zhao
[1] M. Aizerman,et al. Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .
[2] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[3] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[4] Joos Vandewalle,et al. A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..
[5] Gunnar Rätsch,et al. An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.
[6] Andrzej Cichocki,et al. Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression , 2001, Neural Computing & Applications.
[7] Roman Rosipal,et al. Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..
[8] Demetri Terzopoulos,et al. Multilinear Analysis of Image Ensembles: TensorFaces , 2002, ECCV.
[9] Dominik Endres,et al. A new metric for probability distributions , 2003, IEEE Transactions on Information Theory.
[10] Nuno Vasconcelos,et al. A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications , 2003, NIPS.
[11] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[12] Antoni B. Chan,et al. A Family of Probabilistic Kernels Based on Information Divergence , 2004 .
[13] John Shawe-Taylor,et al. Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.
[14] Barbara Caputo,et al. Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..
[15] Yang Li,et al. Kernel-based multifactor analysis for image synthesis and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[16] Kenji Fukumizu,et al. Statistical Convergence of Kernel CCA , 2005, NIPS.
[17] Rajesh P. N. Rao,et al. Learning Shared Latent Structure for Image Synthesis and Robotic Imitation , 2005, NIPS.
[18] Michael I. Jordan,et al. A Probabilistic Interpretation of Canonical Correlation Analysis , 2005 .
[19] Neil D. Lawrence,et al. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..
[20] Juan Carlos Niebles,et al. Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2006, BMVC.
[21] Marios Savvides,et al. Individual Kernel Tensor-Subspaces for Robust Face Recognition: A Computationally Efficient Tensor Framework Without Requiring Mode Factorization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[22] Tae-Kyun Kim,et al. Learning Motion Categories using both Semantic and Structural Information , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Colin Fyfe,et al. Elsevier Editorial System(tm) for Neurocomputing Title: Gaussian Processes for Canonical Correlation Analysis Gaussian Processes for Canonical Correlation Analysis , 2022 .
[24] John Shawe-Taylor,et al. Decomposing the tensor kernel support vector machine for neuroscience data with structured labels , 2010, Machine Learning.
[25] Tae-Kyun Kim,et al. Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[27] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[28] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[29] Trevor Darrell,et al. Factorized Orthogonal Latent Spaces , 2010, AISTATS.
[30] J. Ross Beveridge,et al. Action classification on product manifolds , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[31] Mubarak Shah,et al. Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Jieping Ye,et al. Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Guodong Guo,et al. Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression , 2011, CVPR 2011.
[34] Sheng Tang,et al. Localized Multiple Kernel Learning for Realistic Human Action Recognition in Videos , 2011, IEEE Transactions on Circuits and Systems for Video Technology.
[35] Johan A. K. Suykens,et al. A kernel-based framework to tensorial data analysis , 2011, Neural Networks.
[36] Changsheng Xu,et al. Boosted Exemplar Learning for Action Recognition and Annotation , 2011, IEEE Transactions on Circuits and Systems for Video Technology.
[37] Frank P. Ferrie,et al. A Note on Metric Properties for Some Divergence Measures: The Gaussian Case , 2012, ACML.
[38] Johan A. K. Suykens,et al. Classification of Multichannel Signals With Cumulant-Based Kernels , 2012, IEEE Transactions on Signal Processing.
[39] Tanaya Guha,et al. Learning Sparse Representations for Human Action Recognition , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Thomas B. Moeslund,et al. A Local 3-D Motion Descriptor for Multi-View Human Action Recognition from 4-D Spatio-Temporal Interest Points , 2012, IEEE Journal of Selected Topics in Signal Processing.
[41] Zenglin Xu,et al. Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis , 2011, ICML.
[42] Naotaka Fujii,et al. Higher Order Partial Least Squares (HOPLS): A Generalized Multilinear Regression Method , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.