He, C.[Chao],
Girolami, M.A.[Mark A.],
Ross, G.[Gary],
Employing optimized combinations of one-class classifiers for automated
currency validation,
PR(37), No. 6, June 2004, pp. 1085-1096.
Elsevier DOI
0405
BibRef
Wang, D.,
Yeung, D.S.[Daniel S.],
Tsang, E.C.C.[Eric C.C.],
Structured One-Class Classification,
SMC-B(36), No. 6, December 2006, pp. 1283-1295.
IEEE DOI
0701
BibRef
Angiulli, F.[Fabrizio],
Prototype-Based Domain Description for One-Class Classification,
PAMI(34), No. 6, June 2012, pp. 1131-1144.
IEEE DOI
1205
PDD is a nearest-neighbor classifier.
BibRef
Kwak, N.[Nojun],
Oh, J.Y.[Ji-Yong],
Feature extraction for one-class classification problems:
Enhancements to biased discriminant analysis,
PR(42), No. 1, January 2009, pp. 17-26.
Elsevier DOI
0809
Classification; One-class; One-against-rest; BDA
BibRef
Oh, J.Y.[Ji-Yong],
Kwak, N.[Nojun],
Generalized mean for robust principal component analysis,
PR(54), No. 1, 2016, pp. 116-127.
Elsevier DOI
1603
Generalized mean
BibRef
Oh, J.H.[Jae Hyun],
Kwak, N.[Nojun],
Generalization of linear discriminant analysis using -norm,
PRL(34), No. 6, 15 April 2013, pp. 679-685.
Elsevier DOI
1303
LDA; Norm; Outlier; LDA- L p
BibRef
Oh, J.Y.[Ji-Yong],
Kwak, N.[Nojun],
Lee, M.[Minsik],
Choi, C.H.[Chong-Ho],
Generalized Mean For Feature Extraction in One-Class Classification
Problems,
PR(46), No. 12, 2013, pp. 3328-3340.
Elsevier DOI
1307
Generalized mean
See also Feature Extraction for Classification Problems and Its Application to Face Recognition.
BibRef
Mack, B.[Benjamin],
Roscher, R.[Ribana],
Waske, B.[Björn],
Can I Trust My One-Class Classification?,
RS(6), No. 9, 2014, pp. 8779-8802.
DOI Link
1410
BibRef
Krawczyk, B.[Bartosz],
Wozniak, M.[Michal],
Herrera, F.[Francisco],
On the usefulness of one-class classifier ensembles for decomposition
of multi-class problems,
PR(48), No. 12, 2015, pp. 3969-3982.
Elsevier DOI
1509
One-class classification
BibRef
Krell, M.M.[Mario Michael],
Wöhrle, H.[Hendrik],
New one-class classifiers based on the origin separation approach,
PRL(53), No. 1, 2015, pp. 93-99.
Elsevier DOI
1502
Origin separation approach
BibRef
Liu, J.C.[Jia-Chen],
Miao, Q.G.[Qi-Guang],
Sun, Y.[Yanan],
Song, J.F.[Jian-Feng],
Quan, Y.[Yining],
Fast structural ensemble for One-Class Classification,
PRL(80), No. 1, 2016, pp. 179-187.
Elsevier DOI
1609
One-class classifier
BibRef
Ao, Z.[Zurui],
Su, Y.J.[Yan-Jun],
Li, W.K.[Wen-Kai],
Guo, Q.H.[Qing-Hua],
Zhang, J.[Jing],
One-Class Classification of Airborne LiDAR Data in Urban Areas Using
a Presence and Background Learning Algorithm,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link
1711
BibRef
Son, Y.[Youngdoo],
Lee, S.[Sujee],
Park, S.[Saerom],
Lee, J.W.[Jae-Wook],
Learning representative exemplars using one-class Gaussian process
regression,
PR(74), No. 1, 2018, pp. 185-197.
Elsevier DOI
1711
Representative, exemplars
BibRef
Bicego, M.[Manuele],
Figueiredo, M.A.T.[Mário A.T.],
Clustering via binary embedding,
PR(83), 2018, pp. 52-63.
Elsevier DOI
1808
Clustering, Binary embedding, Finite mixture models,
Biclustering, One-class classification
BibRef
Yu, J.[Jaehong],
Kang, S.[Seokho],
Clustering-based proxy measure for optimizing one-class classifiers,
PRL(117), 2019, pp. 37-44.
Elsevier DOI
1901
One-class classification, One-class classifier,
Hyperparameter optimization, Proxy measure, Model validation,
Pseudo non-target class
BibRef
Perera, P.,
Patel, V.M.,
Learning Deep Features for One-Class Classification,
IP(28), No. 11, November 2019, pp. 5450-5463.
IEEE DOI
1909
Feature extraction, Training, Task analysis, Training data,
Anomaly detection, Authentication, Deep learning,
deep learning
BibRef
Sansone, E.[Emanuele],
de Natale, F.G.B.[Francesco G. B.],
Zhou, Z.H.[Zhi-Hua],
Efficient Training for Positive Unlabeled Learning,
PAMI(41), No. 11, November 2019, pp. 2584-2598.
IEEE DOI
1910
Learn a classifier for a class of interest from an unlabeled data set.
Semisupervised learning, Training, Task analysis, Kernel,
Optimization, Statistical learning, Scalability, Machine learning,
kernel methods
BibRef
Kumar, N.,
Awate, S.P.,
Semi-Supervised Robust Mixture Models in RKHS for Abnormality
Detection in Medical Images,
IP(29), 2020, pp. 4772-4787.
IEEE DOI
2003
Robustness, Training, Semisupervised learning, Kernel, Data models,
Image segmentation, Biomedical imaging, Abnormality detection,
semi-supervised learning
BibRef
Kumar, N.,
Chandran, S.,
Rajwade, A.V.,
Awate, S.P.,
Semi-Supervised Robust One-Class Classification in RKHS for
Abnormality Detection in Medical Images,
ICIP19(544-548)
IEEE DOI
1910
Abnormality detection, one-class classification, kernel,
robust modeling, semi-supervised learning.
BibRef
Zhao, L.[Liya],
Waldner, F.[François],
Scarth, P.[Peter],
Mack, B.[Benjamin],
Hochman, Z.[Zvi],
Combining Fractional Cover Images with One-Class Classifiers Enables
Near Real-Time Monitoring of Fallows in the Northern Grains Region of
Australia,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Mauceri, S.[Stefano],
Sweeney, J.[James],
McDermott, J.[James],
Dissimilarity-based representations for one-class classification on
time series,
PR(100), 2020, pp. 107122.
Elsevier DOI
2005
Dissimilarity-based representations, One-class classification, Time series
BibRef
Xing, H.J.[Hong-Jie],
Liu, Y.J.[Ya-Jie],
He, Z.C.[Zi-Chuan],
Robust sparse coding for one-class classification based on
correntropy and logarithmic penalty function,
PR(111), 2021, pp. 107685.
Elsevier DOI
2012
Sparse coding, One-class classification,
Logarithmic penalty function, Correntropy, One-class support vector machine
BibRef
La Grassa, R.[Riccardo],
Gallo, I.[Ignazio],
Landro, N.[Nicola],
OCmst: One-class novelty detection using convolutional neural network
and minimum spanning trees,
PRL(155), 2022, pp. 114-120.
Elsevier DOI
2203
Novelty detection, Convolutional neural network,
Minimum spanning tree, One-class
BibRef
Noronha, M.D.M.[Marta D.M.],
Henriques, R.[Rui],
Madeira, S.C.[Sara C.],
Zárate, L.E.[Luis E.],
Impact of metrics on biclustering solution and quality: A review,
PR(127), 2022, pp. 108612.
Elsevier DOI
2205
Merit functions, Biclustering evaluation,
Biclustering validation, Bicluster discovery, Systematic review
BibRef
Zaheer, M.Z.[Muhammad Zaigham],
Lee, J.H.[Jin-Ha],
Mahmood, A.[Arif],
Astrid, M.[Marcella],
Lee, S.I.[Seung-Ik],
Stabilizing Adversarially Learned One-Class Novelty Detection Using
Pseudo Anomalies,
IP(31), 2022, pp. 5963-5975.
IEEE DOI
2209
Training, Image reconstruction, Anomaly detection, Generators,
Feature extraction, Detectors, Training data, Novelty detection,
stabilizing adversarial models
BibRef
Lo, S.Y.[Shao-Yuan],
Oza, P.[Poojan],
Patel, V.M.[Vishal M.],
Adversarially Robust One-Class Novelty Detection,
PAMI(45), No. 4, April 2023, pp. 4167-4179.
IEEE DOI
2303
Anomaly detection, Detectors, Principal component analysis,
Robustness, Task analysis, Perturbation methods,
one-class classification
BibRef
Arashloo, S.R.[Shervin Rahimzadeh],
One-Class Classification Using lp-Norm Multiple Kernel Fisher Null
Approach,
IP(32), 2023, pp. 1843-1856.
IEEE DOI
2303
Kernel, Support vector machines, Task analysis, Training,
Optimization, Search problems, Pattern recognition,
lp-norm regularisation
BibRef
La Grassa, R.[Riccardo],
Gallo, I.[Ignazio],
Landro, N.[Nicola],
Dynamic Decision Boundary for One-class Classifiers applied to
non-uniformly Sampled Data,
DICTA20(1-7)
IEEE DOI
2201
Machine learning algorithms, Digital images, Machine learning,
Predictive models, Robustness, Pattern recognition
BibRef
Pourreza, M.[Masoud],
Mohammadi, B.[Bahram],
Khaki, M.[Mostafa],
Bouindour, S.[Samir],
Snoussi, H.[Hichem],
Sabokrou, M.[Mohammad],
G2D: Generate to Detect Anomaly,
WACV21(2002-2011)
IEEE DOI
2106
Training, Computational modeling,
Neural networks, Generative adversarial networks, Generators
BibRef
Larin, A.O.[Aleksandr O.],
Seredin, O.S.[Oleg S.],
Kopylov, A.V.[Andrey V.],
One-class Classification Criterion Robust to Anomalies in Training
Dataset,
IMTA20(155-165).
Springer DOI
2103
BibRef
Choi, S.,
Chang, I.,
Teoh, A.B.J.,
One-class Random Maxout Probabilistic Network for Mobile Touchstroke
Authentication,
ICPR18(3359-3364)
IEEE DOI
1812
Authentication, Probabilistic logic, Training,
Biometrics (access control), Feature extraction, Random maxout
BibRef
Kozerawski, J.,
Turk, M.,
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition,
CVPR18(3446-3455)
IEEE DOI
1812
Training, Support vector machines, Training data, Task analysis,
Convolutional neural networks, Benchmark testing
BibRef
Mygdalis, V.,
Iosifidis, A.,
Tefas, A.,
Pitas, I.,
One class classification applied in facial image analysis,
ICIP16(1644-1648)
IEEE DOI
1610
Computational modeling
BibRef
Aghdam, H.H.[Hamed Habibi],
Heravi, E.J.[Elnaz Jahani],
Puig, D.[Domenec],
A New One Class Classifier Based on Ensemble of Binary Classifiers,
CAIP15(II:242-253).
Springer DOI
1511
BibRef
Hadjadji, B.[Bilal],
Chibani, Y.[Youcef],
Guerbai, Y.[Yasmine],
Multiple One-Class Classifier Combination for Multi-class
Classification,
ICPR14(2832-2837)
IEEE DOI
1412
Accuracy
BibRef
Hadjadji, B.[Bilal],
Chibani, Y.[Youcef],
Nemmour, H.[Hassiba],
Fuzzy Integral Combination of One-Class Classifiers Designed for
Multi-class Classification,
ICIAR14(I: 320-328).
Springer DOI
1410
BibRef
Wang, Q.H.[Qing-Hua],
Lopes, L.S.[Luís Seabra],
Tax, D.M.J.[David M.J.],
Visual Object Recognition Through One-Class Learning,
ICIAR04(I: 463-470).
Springer DOI
0409
BibRef
Tax, D.M.J.,
Muller, K.R.,
A Consistency-Based Model Selection for One-Class Classification,
ICPR04(III: 363-366).
IEEE DOI
0409
See also Component-Based Discriminative Classification for Hidden Markov Models.
BibRef
Hocquet, S.[Sylvain],
Ramel, J.Y.[Jean-Yves],
Carbot, H.[Hubert],
Estimation of User Specific Parameters in One-class Problems,
ICPR06(IV: 449-452).
IEEE DOI
0609
BibRef
Ilonen, J.,
Paalanen, P.,
Kamarainen, J.K.,
Kalviainen, H.,
Gaussian mixture pdf in one-class classification: computing and
utilizing confidence values,
ICPR06(II: 577-580).
IEEE DOI
0609
BibRef
Ercil, A.,
Buke, B.,
One class classification using implicit polynomial surface fitting,
ICPR02(II: 152-155).
IEEE DOI
0211
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
High Dimensional Data, Hyperspectral Data, Hyper-Spectral Data Classification .