Weighted multi-view common subspace learning method

[1]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[2]  Zhihui Li,et al.  Joint Principal Component and Discriminant Analysis for Dimensionality Reduction , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Meng Wang,et al.  A Framework of Joint Low-Rank and Sparse Regression for Image Memorability Prediction , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Nanning Zheng,et al.  Semi-supervised person re-identification using multi-view clustering , 2019, Pattern Recognit..

[5]  Gang Wang,et al.  Graph Multiview Canonical Correlation Analysis , 2018, IEEE Transactions on Signal Processing.

[6]  Guoliang He,et al.  Graph regularized multiview marginal discriminant projection , 2018, J. Vis. Commun. Image Represent..

[7]  Ping Lu,et al.  Person re-identification by enhanced local maximal occurrence representation and generalized similarity metric learning , 2018, Neurocomputing.

[8]  Meng Wang,et al.  Low-Rank Multi-View Embedding Learning for Micro-Video Popularity Prediction , 2018, IEEE Transactions on Knowledge and Data Engineering.

[9]  Wei Yuan,et al.  Multi-view Common Component Discriminant Analysis for Cross-view Classification , 2018, Pattern Recognit..

[10]  Xiao-Yuan Jing,et al.  Multiview Hybrid Embedding: A Divide-and-Conquer Approach , 2018, IEEE Transactions on Cybernetics.

[11]  Huchuan Lu,et al.  Cross-view semantic projection learning for person re-identification , 2018, Pattern Recognit..

[12]  Wai Keung Wong,et al.  Kernelized random KISS metric learning for person re-identification , 2018, Neurocomputing.

[13]  Ping Lu,et al.  Person re-identification by kernel null space marginal Fisher analysis , 2017, Pattern Recognit. Lett..

[14]  Shiliang Sun,et al.  Multi-view learning overview: Recent progress and new challenges , 2017, Inf. Fusion.

[15]  Qi Tian,et al.  Enhancing Micro-video Understanding by Harnessing External Sounds , 2017, ACM Multimedia.

[16]  Zi Huang,et al.  Discrete Multimodal Hashing With Canonical Views for Robust Mobile Landmark Search , 2017, IEEE Transactions on Multimedia.

[17]  Xuelong Li,et al.  Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Liqiang Nie,et al.  Predicting Image Memorability Through Adaptive Transfer Learning From External Sources , 2017, IEEE Transactions on Multimedia.

[19]  Shaogang Gong,et al.  Person Re-Identification by Unsupervised Video Matching , 2016, Pattern Recognit..

[20]  Alberto Del Bimbo,et al.  Multichannel-Kernel Canonical Correlation Analysis for Cross-View Person Reidentification , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[21]  Lin Wu,et al.  Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification , 2016, Pattern Recognit..

[22]  Yi Yang,et al.  Beyond Doctors: Future Health Prediction from Multimedia and Multimodal Observations , 2015, ACM Multimedia.

[23]  Hai Jin,et al.  Landmark Classification With Hierarchical Multi-Modal Exemplar Feature , 2015, IEEE Transactions on Multimedia.

[24]  Hai Jin,et al.  Content-Based Visual Landmark Search via Multimodal Hypergraph Learning , 2015, IEEE Transactions on Cybernetics.

[25]  Yun Fu,et al.  Low-Rank Common Subspace for Multi-view Learning , 2014, 2014 IEEE International Conference on Data Mining.

[26]  Feiping Nie,et al.  Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model , 2013, 2013 IEEE International Conference on Computer Vision.

[27]  Cheng-Lin Liu,et al.  A multi-task framework for metric learning with common subspace , 2013, Neural Computing and Applications.

[28]  Shiguang Shan,et al.  Multi-View Discriminant Analysis , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  David W. Jacobs,et al.  Generalized Multiview Analysis: A discriminative latent space , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Yan-Ning Cai,et al.  On Multiple Kernel Learning Methods: On Multiple Kernel Learning Methods , 2010 .

[31]  John Shawe-Taylor,et al.  Sparse canonical correlation analysis , 2009, Machine Learning.

[32]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[33]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[34]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[35]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Colin Fyfe,et al.  Kernel and Nonlinear Canonical Correlation Analysis , 2000, IJCNN.

[37]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[38]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[39]  Paul Horst,et al.  Relations amongm sets of measures , 1961 .

[40]  Xi Chen,et al.  Structured Sparse Canonical Correlation Analysis , 2012, AISTATS.

[41]  J. Shawe-Taylor,et al.  Multi-View Canonical Correlation Analysis , 2010 .

[42]  Wang Hong.  On Multiple Kernel Learning Methods , 2010 .

[43]  Jing-Yu Yang,et al.  A generalized Foley-Sammon transform based on generalized fisher discriminant criterion and its application to face recognition , 2003, Pattern Recognit. Lett..