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Robust Multiple Manifolds Structure Learning [article]

Dian Gong, Xuemei Zhao (Univ of Southern California), Gerard Medioni
2012 arXiv   pre-print
In the global learning stage, we propose a robust manifold clustering method based on local structure learning results.  ...  We present a robust multiple manifolds structure learning (RMMSL) scheme to robustly estimate data structures under the multiple low intrinsic dimensional manifolds assumption.  ...  We thank Fei Sha for helpful discussions.  ... 
arXiv:1206.4624v1 fatcat:n2wpl2n3fba3nha5crldomghfa

L0-norm Constrained Autoencoders for Unsupervised Outlier Detection [chapter]

Yoshinao Ishii, Satoshi Koide, Keiichiro Hayakawa
2020 Lecture Notes in Computer Science  
We propose a novel unsupervised outlier detection method, L0-norm Constrained Autoencoders (L0-AE), based on an autoencoder-based detector with L0-norm constraints for error terms.  ...  A recent problem in this field is the learning of low-dimensional nonlinear manifolds under L0-norm constraints for error terms.  ...  For example, the Robust Deep Autoencoder (RDA) [24] learns a low-dimensional nonlinear manifold where normal samples are located using an autoencoder (AE) [10] .  ... 
doi:10.1007/978-3-030-47436-2_51 fatcat:wq25df6aizec5o5fwmnul2lyka

Robust locally linear embedding

Hong Chang, Dit-Yan Yeung
2006 Pattern Recognition  
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning community.  ...  In this paper, we address this problem in the context of an NLDR method called locally linear embedding (LLE). Based on robust estimation techniques, we propose an approach to make LLE more robust.  ...  Their local linear smoothing method tries to detect and remove outliers and hence can be used as a preprocessing step before ordinary manifold learning is performed.  ... 
doi:10.1016/j.patcog.2005.07.011 fatcat:axojpklgubbehj2hqe4bzkrql4

Robust Hessian Locally Linear Embedding Techniques for High-Dimensional Data

Xianglei Xing, Sidan Du, Kejun Wang
2016 Algorithms  
Specifically, we first propose a fast outlier detection method for high-dimensional datasets. Then, we employ a local smoothing method to reduce noise.  ...  Despite their appealing properties, most manifold learning algorithms are not robust in practical applications.  ...  Therefore, SLE is more suited for small-scale problems compared to other methods. Conclusions In this paper, we propose the RHLLE (Robust Hessian LLE) method for robust manifold learning.  ... 
doi:10.3390/a9020036 fatcat:xmlxhnuxrzhozg75rhz2ovwqpm

A Multi-criteria Approach for Fast and Outlier-aware Representative Selection from Manifolds [article]

Mahlagha Sedghi, George Atia, Michael Georgiopoulos
2020 arXiv   pre-print
disruptive information by effectively detecting outliers.  ...  MOSAIC's superiority in achieving the desired characteristics of a representative subset all at once while exhibiting remarkable robustness to various outlier types is demonstrated via extensive experiments  ...  We remark that the proposed method offers a stand-alone outlier detection technique for high-dimensional data.  ... 
arXiv:2003.05989v1 fatcat:kqb4a26uvfaehiuzopssyyyk5a

Robust unsupervised motion pattern inference from video and applications

Xuemei Zhao, Gerard Medioni
2011 2011 International Conference on Computer Vision  
Based on tracklets, we use a manifold learning method Tensor Voting to infer the local geometric structures in (x, y) space, and embed tracklet points into (x, y, θ) space, where θ represents motion direction  ...  In this space, points automatically form intrinsic manifold structures, each of which corresponds to a motion pattern. To define each group, a novel robust manifold grouping algorithm is proposed.  ...  We use these tracklets in the training phase to learn motion patterns. Robust Non-linear Manifold Grouping As an important step in motion pattern learning, automatic and robust grouping is needed.  ... 
doi:10.1109/iccv.2011.6126308 dblp:conf/iccv/ZhaoM11 fatcat:p5raw43gnjhmpoxps2udgiw4mi

Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression

Guodong Guo, Yun Fu, C.R. Dyer, T.S. Huang
2008 IEEE Transactions on Image Processing  
In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages.  ...  Index Terms-Age manifold, human age estimation, locally adjusted robust regression, manifold learning, nonlinear regression, support vector machine (SVM), support vector regression (SVR).  ...  Wang for providing their experimental results in [33] and [34] , and they would also like to thank the FG-NET consortium for providing the FG-NET Aging Database [1] . Urbana  ... 
doi:10.1109/tip.2008.924280 pmid:18586625 fatcat:5yktzlkiprevnjboxnguwl2mym

Rejecting Outliers Based on Correspondence Manifold

Xiang-Ru LI, Xiao-Ming LI, Hai-Ling LI, Mao-Yong CAO
2009 Acta Automatica Sinica  
This paper introduce the correspondence manifold and propose a novel scheme to reject outliers by learning upward views of the manifold.  ...  increase of outlier percentage and the number of the estimated model parameters; outlier rejecting is coupled with model selection and model estimation.  ...  Section 3 presents our proposed scheme for outlier rejecting: firstly gives a learning method for correspondence view by a novel group diagnostic technique, and then, introduces an outlier rejecting method  ... 
doi:10.1016/s1874-1029(08)60065-8 fatcat:wcwmqdm5kjgq7i6ptfoftcxque

Robust deformable shape reconstruction from monocular video with manifold forests

Lili Tao, Bogdan J. Matuszewski
2016 Machine Vision and Applications  
The key contributions of this work are the use of random decision forests for the shape manifold learning and robust metric for calculation of the re-projection error.  ...  The learned manifold defines constraints imposed on the reconstructed shapes.  ...  Forest model for manifold learning In the proposed method, the affinity model in manifold learning is built by applying random forest clustering.  ... 
doi:10.1007/s00138-016-0769-3 fatcat:7zg3qbrrbbccdpiarxalrz6sre

Learning Correspondence View with Support Vector Machine

Xiangru Li, Xiaoming Li, Huarong Xu
2009 2009 WRI Global Congress on Intelligent Systems  
The fundamental idea of CV is that, for given two images of a scene, the corresponding points constitute a manifold in joint-image space 4 R , and outliers can be detected by checking whether they are  ...  Experiments on real image pairs demonstrate the excellent performance of our proposed SVM+CV learning method and its superiority over the available robust methods in literature, especially the widely used  ...  Conclusion Outlier rejecting is an important problem in computer vision. And correspondence view (CV) is a recently introduced concept for rejecting outliers.  ... 
doi:10.1109/gcis.2009.402 fatcat:xektc2i7ovcmpgtasoqmrnghhu

Robust Semi-Supervised Manifold Learning Algorithm for Classification

Mingxia Chen, Jing Wang, Xueqing Li, Xiaolong Sun
2018 Mathematical Problems in Engineering  
In this paper, we propose a framework for robust semi-supervised manifold learning (RSSML) to address this problem.  ...  In the recent years, manifold learning methods have been widely used in data classification to tackle the curse of dimensionality problem, since they can discover the potential intrinsic low-dimensional  ...  A fast outlier detection method for high-dimensional data sets is proposed in [8] .  ... 
doi:10.1155/2018/2382803 fatcat:jwjapfqzhvaq3od3w3ydbbeoly

Quadric Hypersurface Intersection for Manifold Learning in Feature Space [article]

Fedor Pavutnitskiy, Sergei O. Ivanov, Evgeny Abramov, Viacheslav Borovitskiy, Artem Klochkov, Viktor Vialov, Anatolii Zaikovskii, Aleksandr Petiushko
2022 arXiv   pre-print
For instance, one may want to automatically mark any point far away from the submanifold as an outlier or to use the geometry to come up with a better distance metric.  ...  At test time, this manifold can be used to introduce a computationally efficient outlier score for arbitrary new data points and to improve a given similarity metric by incorporating the learned geometric  ...  Motivated by the problem of feature space level outlier detection and similarity metric improvement, we propose a manifold learning technique where the manifold is learned in form of an intersection of  ... 
arXiv:2102.06186v2 fatcat:qzr6ipuz6refdawnhnicojdw7y

A geometric perspective on functional outlier detection [article]

Moritz Herrmann, Fabian Scheipl
2021 arXiv   pre-print
This improves practical feasibility of functional outlier detection: We show that simple manifold learning methods can be used to reliably infer and visualize the geometric structure of functional data  ...  We also show that standard outlier detection methods requiring tabular data inputs can be applied to functional data very successfully by simply using their vector-valued representations learned from manifold  ...  The authors of this work take full responsibility for its content.  ... 
arXiv:2109.06849v1 fatcat:eyfqqcoylnep5f3a4livn7hg7u

Probabilistic tensor voting for robust perceptual grouping

Dian Gong, Gerard Medioni
2012 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  
Experimental results show that our approach outperforms other methods, including standard tensor voting.  ...  Probabilistic tensor voting explicitly considers both outlier and inlier noise, and can handle them simultaneously.  ...  The authors would like to thank Philippos Mordohai and Fei Sha for their helpful discussions.  ... 
doi:10.1109/cvprw.2012.6238926 dblp:conf/cvpr/GongM12 fatcat:nci67lf2efbrpllgs4my6uvfxi

Carrying Object Detection Using Pose Preserving Dynamic Shape Models [chapter]

Chan-Su Lee, Ahmed Elgammal
2006 Lecture Notes in Computer Science  
In this paper, we introduce a framework for carrying object detection in different people from different views using pose preserving dynamic shape models.  ...  Iterative estimation of shape style and view with pose preserving generative model allows estimation of outlier in addition to accurate body pose.  ...  Carrying Object Detection Using Pose Preserving Dynamic Shape Models  ... 
doi:10.1007/11789239_33 fatcat:peyxdaz4szeejdl5m6pbuntcwa
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