Rousseeuw, P.J.,
Robust Regression and Outlier Detection,
John
Wiley&Sons, New York, 1987.
BibRef
8700
Rousseeuw, P.J.,
Least Median of Squares Regression,
ASAJ(79), 1984, pp. 871-880.
BibRef
8400
Urahama, K.,
Furukawa, Y.,
Gradient descent learning of nearest neighbor classifiers with outlier
rejection,
PR(28), No. 5, May 1995, pp. 761-768.
Elsevier DOI
0401
BibRef
Black, M.J.,
Rangarajan, A.,
On The Unification of Line Processes, Outlier Rejection, and
Robust Statistics with Applications in Early Vision,
IJCV(19), No. 1, July 1996, pp. 57-91.
Springer DOI
PDF File.
9608
BibRef
Earlier:
The Outlier Process: Unifying Line Processes and Robust Statistics,
CVPR94(15-22).
IEEE DOI Applied to reconstruction of degraded images.
BibRef
Kharin, Y.[Yurij],
Zhuk, E.[Eugene],
Filtering of multivariate samples containing 'outliers' for clustering,
PRL(19), No. 12, 30 October 1998, pp. 1077-1085.
BibRef
9810
Earlier:
Robustness in statistical pattern recognition under 'contaminations' of
training samples,
ICPR94(B:504-506).
IEEE DOI
9410
BibRef
Jiang, M.F.,
Tseng, S.S.,
Su, C.M.,
Two-phase clustering process for outliers detection,
PRL(22), No. 6-7, May 2001, pp. 691-700.
Elsevier DOI
0105
BibRef
Ramaswamy, S.[Sridhar],
Rastogi, R.[Rajeev],
Shim, K.[Kyuseok],
Efficient algorithms for mining outliers from large data sets,
ACM SIGMOD(29), No. 2, June 2000, pp. 427-438.
WWW Link.
Formulation for distance based outliers.
BibRef
0006
Miller, D.J.,
Browning, J.,
A mixture model and EM-based algorithm for class discovery, robust
classification, and outlier rejection in mixed labeled/unlabeled data
sets,
PAMI(25), No. 11, November 2003, pp. 1468-1483.
IEEE Abstract.
0311
Augment the training set with unlabeled examples, assumed to come from
a know class or a completely new class.
Robust analysis.
BibRef
He, Z.Y.[Zeng-You],
Xu, X.F.[Xiao-Fei],
Deng, S.C.[Sheng-Chun],
Discovering cluster-based local outliers,
PRL(24), No. 9-10, June 2003, pp. 1641-1650.
Elsevier DOI
0304
BibRef
Shekhar, S.[Shashi],
Lu, C.T.[Chang-Tien],
Zhang, P.S.[Pu-Sheng],
A Unified Approach to Detecting Spatial Outliers,
GeoInfo(7), No. 2, June 2003, pp. 139-166.
DOI Link
0307
BibRef
Hu, T.M.[Tian-Ming],
Sung, S.Y.[Sam Y.],
Detecting pattern-based outliers,
PRL(24), No. 16, December 2003, pp. 3059-3068.
Elsevier DOI
0310
BibRef
Zhang, J.S.[Jiang-She],
Leung, Y.W.[Yiu-Wing],
Robust clustering by pruning outliers,
SMC-B(33), No. 6, December 2003, pp. 983-999.
IEEE Abstract.
0401
BibRef
Grinstead, B.[Brad],
Koschan, A.F.[Andreas F.],
Gribok, A.V.[Andrei V.],
Abidi, M.A.[Mongi A.],
Gorsich, D.[David],
Outlier rejection by oriented tracks to aid pose estimation from video,
PRL(27), No. 1, 1 January 2006, pp. 37-48.
Elsevier DOI
0512
BibRef
Chang, H.[Hong],
Yeung, D.Y.[Dit-Yan],
Robust locally linear embedding,
PR(39), No. 6, June 2006, pp. 1053-1065.
Elsevier DOI Nonlinear dimensionality reduction; Manifold learning;
Locally linear embedding; Principal component analysis; Outlier;
Robust statistics; M-estimation;
Handwritten digit; Wood texture
0604
BibRef
Kim, J.H.[Jae-Hak],
Han, J.H.[Joon H.],
Outlier correction from uncalibrated image sequence using the
Triangulation method,
PR(39), No. 3, March 2006, pp. 394-404.
Elsevier DOI
0601
BibRef
Hautamaki, V.,
Karkkainen, I.,
Franti, P.,
Outlier detection using k-nearest neighbour graph,
ICPR04(III: 430-433).
IEEE DOI
0409
BibRef
Bandyopadhyay, S.[Sanghamitra],
Santra, S.[Santanu],
A genetic approach for efficient outlier detection in projected space,
PR(41), No. 4, April 2008, pp. 1338-1349.
Elsevier DOI
0801
Deviation detection; Gene expression; Genetic algorithm;
Grid count tree; Projected dimension; Outlier
BibRef
Zhang, J.F.[Ji-Fu],
Jiang, Y.Y.[Yi-Yong],
Chang, K.H.[Kai H.],
Zhang, S.[Sulan],
Cai, J.H.[Jiang-Hui],
Hu, L.H.[Li-Hua],
A concept lattice based outlier mining method in low-dimensional
subspaces,
PRL(30), No. 15, 1 November 2009, pp. 1434-1439.
Elsevier DOI
0910
Outliers; Concept lattice; Sparsity coefficient; Density coefficient;
Intent reduction
BibRef
Chen, Y.X.[Yi-Xin],
Dang, X.[Xin],
Peng, H.X.[Han-Xiang],
Bart, Jr., H.L.[Henry L.],
Outlier Detection with the Kernelized Spatial Depth Function,
PAMI(31), No. 2, February 2009, pp. 288-305.
IEEE DOI
0901
Outliers in input data.
BibRef
Lee, H.J.[Hyun-Jung],
Seo, Y.D.[Yong-Duek],
Lee, S.W.[Sang Wook],
Removing outliers by minimizing the sum of infeasibilities,
IVC(28), No. 6, June 2010, pp. 881-889.
Elsevier DOI
1003
The L-infinity optimization; Outlier removal; The sum of infeasibilities
BibRef
Szeto, C.C.[Chi-Cheong],
Hung, E.[Edward],
Mining outliers with faster cutoff update and space utilization,
PRL(31), No. 11, 1 August 2010, pp. 1292-1301.
Elsevier DOI
1008
Outlier detection; Distance-based outliers; Disk-based algorithms;
Memory optimization
See also Efficient algorithms for mining outliers from large data sets.
BibRef
Zhang, T.,
Huang, K.,
Li, X.,
Yang, J.,
Tao, D.,
Discriminative Orthogonal Neighborhood-Preserving Projections for
Classification,
SMC-B(40), No. 1, February 2010, pp. 253-263.
IEEE DOI
0911
To overcome outlier problems in linear embedded classification.
BibRef
Jiang, F.[Feng],
Sui, Y.F.[Yue-Fei],
Cao, C.[Cungen],
A hybrid approach to outlier detection based on boundary region,
PRL(32), No. 14, 15 October 2011, pp. 1860-1870.
Elsevier DOI
1110
Outlier detection; Rough sets; Boundary; Distance; KDD
BibRef
Yu, S.[Stella],
Angular Embedding: A Robust Quadratic Criterion,
PAMI(34), No. 1, January 2012, pp. 158-173.
IEEE DOI
1112
given pairwise local ordering, find global ordering.
Outlier removal.
BibRef
Zhao, J.[Ji],
Ma, J.[Jiayi],
Tian, J.W.[Jin-Wen],
Ma, J.[Jie],
Zhang, D.[Dazhi],
A robust method for vector field learning with application to mismatch
removing,
CVPR11(2977-2984).
IEEE DOI
1106
Vector Field Consensus (VFC).
Distinguish inliers from outliers.
BibRef
Daneshpazhouh, A.[Armin],
Sami, A.[Ashkan],
Entropy-based outlier detection using semi-supervised approach with
few positive examples,
PRL(49), No. 1, 2014, pp. 77-84.
Elsevier DOI
1410
Data mining
BibRef
Rasheed, F.,
Alhajj, R.,
A Framework for Periodic Outlier Pattern Detection in Time-Series
Sequences,
Cyber(44), No. 5, May 2014, pp. 569-582.
IEEE DOI
1405
data mining
BibRef
Ru, X.H.[Xiao-Hu],
Liu, Z.[Zheng],
Huang, Z.T.[Zhi-Tao],
Jiang, W.L.[Wen-Li],
Normalized residual-based constant false-alarm rate outlier detection,
PRL(69), No. 1, 2016, pp. 1-7.
Elsevier DOI
1601
Outlier detection
BibRef
Domingues, R.[Rémi],
Filippone, M.[Maurizio],
Michiardi, P.[Pietro],
Zouaoui, J.[Jihane],
A comparative evaluation of outlier detection algorithms:
Experiments and analyses,
PR(74), No. 1, 2018, pp. 406-421.
Elsevier DOI
1711
Outlier detection
BibRef
Xu, Z.[Zhi],
Cai, G.Y.[Guo-Yong],
Wen, Y.M.[Yi-Min],
Chen, D.D.[Dong-Dong],
Han, L.Y.[Li-Yao],
Image set-based classification using collaborative exemplars
representation,
SIViP(12), No. 4, May 2018, pp. 607-615.
Springer DOI
1805
Represent the image sets and deal with outliers.
BibRef
Qi, N.X.[Nai-Xin],
Zhang, S.X.[Sheng-Xiu],
Cao, L.J.[Li-Jia],
Yang, X.G.[Xiao-Gang],
Li, C.X.[Chuan-Xiang],
He, C.[Chuan],
Fast and robust homography estimation method with algebraic outlier
rejection,
IET-IPR(12), No. 4, April 2018, pp. 552-562.
DOI Link
1804
Different characteristic in errors between inliers and outliers.
BibRef
Ning, J.[Jin],
Chen, L.[Leiting],
Zhou, C.[Chuan],
Wen, Y.[Yang],
Parameter k search strategy in outlier detection,
PRL(112), 2018, pp. 56-62.
Elsevier DOI
1809
Parameter k, Outlier detection, Mutual neighbor graph
BibRef
Chakraborty, D.[Debasrita],
Narayanan, V.[Vaasudev],
Ghosh, A.[Ashish],
Integration of deep feature extraction and ensemble learning for
outlier detection,
PR(89), 2019, pp. 161-171.
Elsevier DOI
1902
Deep learning, Autoencoders, Probabilistic neural networks,
Ensemble learning, Outlier detection
BibRef
Riani, M.[Marco],
Atkinson, A.C.[Anthony C.],
Cerioli, A.[Andrea],
Corbellini, A.[Aldo],
Efficient robust methods via monitoring for clustering and
multivariate data analysis,
PR(88), 2019, pp. 246-260.
Elsevier DOI
1901
Bovine phlegmon, Car-bike plot, Clustering,
Eigenvalue constraint, Forward search, MCD, MM-Estimation, Outliers
BibRef
Dutta, J.K.[Jayanta K.],
Banerjee, B.[Bonny],
Improved outlier detection using sparse coding-based methods,
PRL(122), 2019, pp. 99-105.
Elsevier DOI
1904
Outlier detection, Outlier scoring, High dimension, Difficulty level
BibRef
Blouvshtein, L.[Leonid],
Cohen-Or, D.[Daniel],
Outlier Detection for Robust Multi-Dimensional Scaling,
PAMI(41), No. 9, Sep. 2019, pp. 2273-2279.
IEEE DOI
1908
Image edge detection, Histograms, Robustness, Data visualization,
Distortion, Tuning, Cognition, Multidimensional scaling, outliers,
data visualization
BibRef
Ma, J.Y.[Jia-Yi],
Jiang, X.Y.[Xing-Yu],
Jiang, J.J.[Jun-Jun],
Guo, X.J.[Xiao-Jie],
Robust Feature Matching Using Spatial Clustering With Heavy Outliers,
IP(29), No. 1, 2020, pp. 736-746.
IEEE DOI
1910
Task analysis, Clustering methods, Databases,
Pattern matching, Complexity theory,
mismatch removal
BibRef
Slavakis, K.[Konstantinos],
Banerjee, S.[Sinjini],
Robust Hierarchical-Optimization RLS Against Sparse Outliers,
SPLetters(27), 2020, pp. 171-175.
IEEE DOI
2002
Recursive Least Squares.
RLS, robust, outliers, sparsity
BibRef
Kauffmann, J.[Jacob],
Müller, K.R.[Klaus-Robert],
Montavon, G.[Grégoire],
Towards explaining anomalies:
A deep Taylor decomposition of one-class models,
PR(101), 2020, pp. 107198.
Elsevier DOI
2003
Outlier detection, Explainable machine learning,
Deep Taylor decomposition, Kernel machines, Unsupervised learning
BibRef
Rofatto, V.F.[Vinicius Francisco],
Matsuoka, M.T.[Marcelo Tomio],
Klein, I.[Ivandro],
Veronez, M.R.[Maurício Roberto],
da Silveira, L.G.[Luiz Gonzaga],
A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link
2003
IDS: Iterative Data Snooping.
BibRef
Goh, M.J.S.[Michael Joon Seng],
Chiew, Y.S.[Yeong Shiong],
Foo, J.J.[Ji Jinn],
Outlier percentage estimation for shape- and parameter-independent
outlier detection,
IET-IPR(14), No. 14, December 2020, pp. 3414-3421.
DOI Link
2012
BibRef
Yu, Q.,
Aizawa, K.,
Unknown Class Label Cleaning For Learning With Open-Set Noisy Labels,
ICIP20(1731-1735)
IEEE DOI
2011
Noise measurement, Training, Training data, Cleaning, Optimization,
Neural networks, Robustness, Noisy label, label cleaning,
open-set image classification
BibRef
Cavalli, L.[Luca],
Larsson, V.[Viktor],
Oswald, M.R.[Martin Ralf],
Sattler, T.[Torsten],
Pollefeys, M.[Marc],
Handcrafted Outlier Detection Revisited,
ECCV20(XIX:770-787).
Springer DOI
2011
BibRef
Kwon, G.[Gukyeong],
Prabhushankar, M.[Mohit],
Temel, D.[Dogancan],
AlRegib, G.[Ghassan],
Backpropagated Gradient Representations for Anomaly Detection,
ECCV20(XXI:206-226).
Springer DOI
2011
BibRef
Wu, X.[Xin],
Cai, L.[Ling],
Ji, R.R.[Rong-Rong],
Gamma Mixture Models for Outlier Removal,
ICIP18(828-832)
IEEE DOI
1809
Outlier in training samples.
Training, Boosting, Probability, Mixture models, Probabilistic logic,
Task analysis, Gamma Mixture Model, Outlier Removal,
Deep Neural Network
BibRef
You, C.,
Robinson, D.P.,
Vidal, R.,
Provable Self-Representation Based Outlier Detection in a Union of
Subspaces,
CVPR17(4323-4332)
IEEE DOI
1711
Anomaly detection, Markov processes,
Principal component analysis, Robustness, Sparse matrices, Tools
BibRef
Piotto, N.[Nicola],
Cordara, G.[Giovanni],
Statistical modelling for enhanced outlier detection,
ICIP14(4280-4284)
IEEE DOI
1502
Covariance matrices
BibRef
Liu, W.[Wei],
Hua, G.[Gang],
Smith, J.R.[John R.],
Unsupervised One-Class Learning for Automatic Outlier Removal,
CVPR14(3826-3833)
IEEE DOI
1409
One-Class Learning; Outlier Removal
BibRef
Lee, K.H.[Kwang Hee],
Lee, S.W.[Sang Wook],
Deterministic Fitting of Multiple Structures Using Iterative MaxFS
with Inlier Scale Estimation,
ICCV13(41-48)
IEEE DOI
1403
MaxFS; fitting of multiple strucutres; inlier scale
Robust fitting with outliers.
BibRef
Goldstein, M.[Markus],
FastLOF: An Expectation-Maximization based Local Outlier detection
algorithm,
ICPR12(2282-2285).
WWW Link.
1302
BibRef
Fritsch, V.[Virgile],
Varoquaux, G.[Gaël],
Poline, J.B.[Jean-Baptiste],
Thirion, B.[Bertrand],
Non-parametric Density Modeling and Outlier-Detection in Medical
Imaging Datasets,
MLMI12(210-217).
Springer DOI
1211
BibRef
Gao, Y.[Yan],
Li, Y.Q.[Yi-Qun],
Improving Gaussian Process Classification with Outlier Detection, with
Applications in Image Classification,
ACCV10(IV: 153-164).
Springer DOI
1011
BibRef
Seo, Y.D.[Yong-Duek],
Lee, H.J.[Hyun-Jung],
Lee, S.W.[Sang Wook],
Outlier Removal by Convex Optimization for L-Infinity Approaches,
PSIVT09(203-214).
Springer DOI
0901
BibRef
Tax, D.M.J.[David M. J.],
Juszczak, P.[Piotr],
Pekalska, E.[Elÿzbieta],
Duin, R.P.W.[Robert P. W.],
Outlier Detection Using Ball Descriptions with Adjustable Metric,
SSPR06(587-595).
Springer DOI
0608
BibRef
Colliez, J.,
Dufrenois, F.,
Hamad, D.,
Robust Regression and Outlier Detection with SVR:
Application to Optic Flow Estimation,
BMVC06(III:1229).
PDF File.
0609
BibRef
Sim, K.[Kristy],
Hartley, R.[Richard],
Removing Outliers Using The L-inf Norm,
CVPR06(I: 485-494).
IEEE DOI
0606
See also Recovering Camera Motion Using L-inf Minimization.
BibRef
Hautamäki, V.[Ville],
Cherednichenko, S.[Svetlana],
Kärkkäinen, I.[Ismo],
Kinnunen, T.[Tomi],
Fränti, P.[Pasi],
Improving K-Means by Outlier Removal,
SCIA05(978-987).
Springer DOI
0506
BibRef
den Hollander, R.J.M.,
Hanjalic, A.,
Outlier identification in stereo correspondences using quadrics,
BMVC05(xx-yy).
HTML Version.
0509
Robust method for computing epipolar geometry from matches.
BibRef
Park, J.H.[Jin-Hyeong],
Zhang, Z.Y.[Zhen-Yue],
Zha, H.Y.[Hong-Yuan],
Kasturi, R.,
Local smoothing for manifold learning,
CVPR04(II: 452-459).
IEEE DOI
0408
Weighted smoothing for outlier detection.
Build on weighted PCA.
BibRef
Brailovsky, V.L.,
An Approach to Outlier Detection Based on Bayesian Probabilistic Model,
ICPR96(II: 70-74).
IEEE DOI
9608
(Tel-Aviv Univ., IL)
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Boosting, AdaBoost Technique .