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Spectral regression

Deng Cai, Xiaofei He, Jiawei Han
2007 Proceedings of the 15th international conference on Multimedia - MULTIMEDIA '07  
In this paper, by using techniques from spectral graph embedding and regression, we propose a unified framework, called spectral regression, for learning an image subspace.  ...  And more crucially, it provides much faster computation and therefore makes the retrieval system capable of responding to the user's query more efficiently.  ...  Acknowledgments We would like to thank Xinjing Wang at MSRA for providing the data. The work was supported in part by the U.S.  ... 
doi:10.1145/1291233.1291329 dblp:conf/mm/CaiHH07 fatcat:yaoujh7kkfgy7jx5nu7bc3j2nm

Representation Learning with Deep Extreme Learning Machines for Efficient Image Set Classification [article]

Muhammad Uzair, Faisal Shafait, Bernard Ghanem, Ajmal Mian
2015 arXiv   pre-print
We learn the non-linear structure of image sets with Deep Extreme Learning Machines (DELM) that are very efficient and generalize well even on a limited number of training samples.  ...  Efficient and accurate joint representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification.  ...  {β i } n h i=1 ) are learned efficiently using regularized least squares.  ... 
arXiv:1503.02445v3 fatcat:2x3pddwt3jagldi2asogmgnvlq

Graph-Oriented Learning via Automatic Group Sparsity for Data Analysis

Yuqiang Fang, Ruili Wang, Bin Dai
2012 2012 IEEE 12th International Conference on Data Mining  
Furthermore, we integrate the proposed graph with several graph-oriented learning algorithms: spectral embedding, spectral clustering, subspace learning and manifold regularized non-negative matrix factorization  ...  In order to overcome such limitation, we propose a new method of constructing an informative graph using automatic group sparse regularization based on the work of ℓ 1 -Graph, which is called as group  ...  The authors would like to thank the anonymous referees for their helpful comments and suggestions. This research is supported by the National Nature Science Foundation of China (61075043).  ... 
doi:10.1109/icdm.2012.82 dblp:conf/icdm/FangWD12 fatcat:y5hhme43yvajrat6l25jvjy3qe

Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Jian Xu, Xiao Xiang Zhu
2019 ISPRS journal of photogrammetry and remote sensing (Print)  
IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized  ...  A semi-supervised HDR approach, called iterative multitask regression (IMR), is proposed in this paper to address this need.  ...  Acknowledgements The authors would like to thank the Hyperspectral Image Analysis group and the NSF Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the CASI  ... 
doi:10.1016/j.isprsjprs.2019.09.008 pmid:31853165 pmcid:PMC6894308 fatcat:lq47lp5qnjewzh5zzpwfk2ynxm

Fast semi-supervised discriminant analysis for binary classification of large data sets

Joris Tavernier, Jaak Simm, Karl Meerbergen, Joerg Kurt Wegner, Hugo Ceulemans, Yves Moreau
2019 Pattern Recognition  
These methods are based on Krylov subspace methods which exploit the data sparsity and the shift-invariance of Krylov subspaces.  ...  We propose and analyze three scalable and related algorithms for semi-supervised discriminant analysis (SDA).  ...  Acknowledgement The authors would like to thank Jean-Marc Neefs for annotating assays with protein and gene identifiers and Vladimir Chupakhin for helping with the compound normalization of the Janssen  ... 
doi:10.1016/j.patcog.2019.02.015 fatcat:po2watge25gzlj53gri7eny2fu

Are standard sample measurements still needed to transfer multivariate calibration models between near-infrared spectrometers? The answer is not always

Puneet Mishra, Ramin Nikzad-Langerodi, Federico Marini, Jean Michel Roger, Alessandra Biancolillo, Douglas N. Rutledge, Santosh Lohumi
2021 TrAC. Trends in analytical chemistry  
Particularly this work explains some newer concepts for standard-free CT, where the standard samples are not required to attain the CT.  ...  Nikzad-Langerodi acknowledges support by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Digital and Economic Affairs (  ...  BMDW), and the Province of Upper Austria in the frame of the COMET -Competence Centers for Excellent Technologies programme managed by Austrian Research Promotion Agency FFG and the COMET Center CHASE.  ... 
doi:10.1016/j.trac.2021.116331 fatcat:w6iqorvv3bbire53crb6ihfvya

Head pose estimation using Spectral Regression Discriminant Analysis

Caifeng Shan, Wei Chen
2009 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  
In this paper, we investigate a recently proposed efficient subspace learning method, Spectral Regression Discriminant Analysis (SRDA), and its kernel version SRKDA for head pose estimation.  ...  Moreover, our approach for estimating the regularization parameter is shown to be effective in head pose estimation and face recognition experiments.  ...  [4, 3, 5] proposed an efficient subspace learning algorithm, Spectral Regression Discriminant Analysis (SRDA).  ... 
doi:10.1109/cvpr.2009.5204261 fatcat:lxjxzmhbiff5tnys737h2yemgq

Head pose estimation using Spectral Regression Discriminant Analysis

Caifeng Shan, Wei Chen
2009 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  
In this paper, we investigate a recently proposed efficient subspace learning method, Spectral Regression Discriminant Analysis (SRDA), and its kernel version SRKDA for head pose estimation.  ...  Moreover, our approach for estimating the regularization parameter is shown to be effective in head pose estimation and face recognition experiments.  ...  [4, 3, 5] proposed an efficient subspace learning algorithm, Spectral Regression Discriminant Analysis (SRDA).  ... 
doi:10.1109/cvprw.2009.5204261 dblp:conf/cvpr/ShanC09 fatcat:7oouvcva5ja5rfix3ssqbp6qbi

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

José M. Bioucas-Dias, Antonio Plaza, Nicolas Dobigeon, Mario Parente, Qian Du, Paul Gader, Jocelyn Chanussot
2012 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical  ...  Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described.  ...  Green and the AVIRIS team for making the Rcuprite hyperspectral data set available to the community, and the United States Geological Survey (USGS) for their publicly available library of mineral signatures  ... 
doi:10.1109/jstars.2012.2194696 fatcat:s66a35xjd5dqzkw5wwihq6ux64

Sparse-Dense Subspace Clustering [article]

Shuai Yang, Wenqi Zhu, Yuesheng Zhu
2019 arXiv   pre-print
We show that IMC is efficient when clustering large-scale data, and PCE ensures better performance for IMC. We show the universality of our SDSC framework as well.  ...  Finally we extend our work into a Sparse-Dense Subspace Clustering (SDSC) framework with a dense stage to optimize the affinity matrix for two-stage methods.  ...  Least Squares Regression (LSR) (Lu et al. 2012) and Efficient Dense Subspace Clustering (EDSC) (Ji, Salzmann, and Li 2014) uses the 2 norm regularization on C.  ... 
arXiv:1910.08909v1 fatcat:my2kahityrgf7jkwjqpl6p3wta

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [article]

Danfeng Hong and Wei He and Naoto Yokoya and Jing Yao and Lianru Gao and Liangpei Zhang and Jocelyn Chanussot and Xiao Xiang Zhu
2021 arXiv   pre-print
However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity  ...  For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications.  ...  • spectral unmixing, • cross-modality learning for large-scale land cover mapping.  ... 
arXiv:2103.01449v1 fatcat:jvo4pr5atvfb5kohpslvkhhmky

Introduction to the Issue on Robust Subspace Learning and Tracking: Theory, Algorithms, and Applications

T. Bouwmans, N. Vaswani, P. Rodriguez, R. Vidal, Z. Lin
2018 IEEE Journal on Selected Topics in Signal Processing  
For this, a higher numerical efficiency is provided by defining analytical SSA variants while a higher robustness is obtained by utilizing To be robust against spectral variability in inverse problems  ...  efficient denoising techniques for multi-frame images and video.  ... 
doi:10.1109/jstsp.2018.2879245 fatcat:z3ohqdl37nat3pjo65fzsf2ady

Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features

Yuntao Qian, Minchao Ye, Jun Zhou
2013 IEEE Transactions on Geoscience and Remote Sensing  
to model the data for regression and classification purpose.  ...  In a similar manner, Li et al combined the posterior class densities, which are generated by a subspace multinomial logistic regression classifier, and spatial contextual information that is represented  ...  This method has shown some very distinct characteristics that are extremely suitable for hyperspectral classification, including high classification accuracy, computational efficiency, favorable generalization  ... 
doi:10.1109/tgrs.2012.2209657 fatcat:t2jc33vmanhihfy7wwcm25wb6m

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [article]

José M. Bioucas-Dias, Antonio Plaza, Nicolas Dobigeon, Mario Parente, Qian Du, Paul Gader, Jocelyn Chanussot
2012 arXiv   pre-print
Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical  ...  Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described.  ...  Green and the AVIRIS team for making the Rcuprite hyperspectral data set available to the community, and the United States Geological Survey (USGS) for their publicly available library of mineral signatures  ... 
arXiv:1202.6294v2 fatcat:4vxq62jxvzfynpb75wvvhw4phq

Spectral Algorithm for Low-rank Multitask Regression [article]

Yotam Gigi, Ami Wiesel, Sella Nevo, Gal Elidan, Avinatan Hassidim, Yossi Matias
2019 arXiv   pre-print
Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learning.  ...  We overcome this hurdle and provide a provably beneficial non-iterative spectral algorithm.  ...  Random Features: Practically, CMR can be used as a natural extension to extreme learning machines or regression with random features [11, 23] .  ... 
arXiv:1910.12204v1 fatcat:2bjx5hg3jnd53km436y76qixwa
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