14.3.1 Outlier Detection and Analysis, Robust Analysis

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

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

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

Traun, C.[Christoph], Schreyer, M.L.[Manuela Larissa], Wallentin, G.[Gudrun],
Empirical Insights from a Study on Outlier Preserving Value Generalization in Animated Choropleth Maps,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Mukhriya, A.[Akanksha], Kumar, R.[Rajeev],
Building outlier detection ensembles by selective parameterization of heterogeneous methods,
PRL(146), 2021, pp. 126-133.
Elsevier DOI 2105
Outlier detection, Ensemble learning, Member selection, Parameterization, Accuracy-diversity trade-off BibRef

Lin, C.[Chuang], Guo, S.X.[Shan-Xin], Chen, J.S.[Jin-Song], Sun, L.[Luyi], Zheng, X.[Xiaorou], Yang, Y.[Yan], Xiong, Y.[Yingfei],
Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wang, P.[Peng], Niu, Y.X.[Yan-Xiong], Xiong, R.[Rui], Ma, F.[Fu], Zhang, C.X.[Chun-Xi],
DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Mensi, A.[Antonella], Bicego, M.[Manuele],
Enhanced anomaly scores for isolation forests,
PR(120), 2021, pp. 108115.
Elsevier DOI 2109
Anomaly detection, Isolation forest, Anomaly score, Outliers BibRef

Marson, L.[Luca], Li, V.[Vladimir], Maki, A.[Atsuto],
Boundary Optimised Samples Training for Detecting Out-of-Distribution Images,
ICPR21(10486-10492)
IEEE DOI 2105
Training, Measurement, Toy manufacturing industry, Training data, Data visualization, Benchmark testing, Propagation losses BibRef

Sharma, K.[Karishma], Donmez, P.[Pinar], Luo, E.[Enming], Liu, Y.[Yan], Yalniz, I.Z.[I. Zeki],
Noiserank: Unsupervised Label Noise Reduction with Dependence Models,
ECCV20(XXVII:737-753).
Springer DOI 2011
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 .