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Spectral regression
2007
Proceedings of the 15th international conference on Multimedia - MULTIMEDIA '07
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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]
2015
arXiv
pre-print
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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
2012
2012 IEEE 12th International Conference on Data Mining
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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
2019
ISPRS journal of photogrammetry and remote sensing (Print)
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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
2019
Pattern Recognition
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2017 pre-print arXiv:1709.04794v1 fatcat:2mrq3adtfzegtfa5iyoflghfnmOther Versions
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
2021
TrAC. Trends in analytical chemistry
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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
2009
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
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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
2009
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
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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
2012
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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]
2019
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2018
IEEE Journal on Selected Topics in Signal Processing
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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
2013
IEEE Transactions on Geoscience and Remote Sensing
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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]
2012
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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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