16.7.3.3.5 Novelty Detection

Chapter Contents (Back)
Novelty Detection.

Augusteijn, M.F., Folkert, B.A.,
Neural network classification and novelty detection,
JRS(23), No. 14, July 2002, pp. 2891-2902. 0208
BibRef

Cao, L.J.[Li Juan], Lee, H.P.[Heow Pueh], Chong, W.K.[Wai Keong],
Modified support vector novelty detector using training data with outliers,
PRL(24), No. 14, October 2003, pp. 2479-2487.
Elsevier DOI 0307
BibRef

He, C.[Chao], Girolami, M.A.[Mark A.],
Novelty detection employing an L2 optimal non-parametric density estimator,
PRL(25), No. 12, September 2004, pp. 1389-1397.
Elsevier DOI 0409
Reduced set density estimator. Binary classification. BibRef

Lee, H.J.[Hyoung-Joo], Cho, S.Z.[Sung-Zoon],
Application of LVQ to novelty detection using outlier training data,
PRL(27), No. 13, 1 October 2006, pp. 1572-1579.
Elsevier DOI Novelty detection; Outlier detection; Novel data; Codebook methods; Self-organizing maps; Learning vector quantization 0606
BibRef

Camci, F.[Fatih], Chinnam, R.B.[Ratna Babu],
General support vector representation machine for one-class classification of non-stationary classes,
PR(41), No. 10, October 2008, pp. 3021-3034.
Elsevier DOI 0808
Novelty detection; One-class classification; Support vector machine; Non-stationary classes; Non-stationary processes; Online training; Outlier detection BibRef

Wu, M.R.[Ming-Rui], Ye, J.P.[Jie-Ping],
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers,
PAMI(31), No. 11, November 2009, pp. 2088-2092.
IEEE DOI 0910
BibRef

Quinn, J.A.[John A.], Williams, C.K.I.[Christopher K.I.], McIntosh, N.[Neil],
Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring,
PAMI(31), No. 9, September 2009, pp. 1537-1551.
IEEE DOI 0907
BibRef
Earlier: A1, A2, Only:
Known Unknowns: Novelty Detection in Condition Monitoring,
IbPRIA07(I: 1-6).
Springer DOI 0706
Kalman filter in time series analysis. Analysis of systems with unknown factors that switch between states. ICU monitoring data. BibRef

Hansen, M.S.[Michael Sass], Sjostrand, K.[Karl], Larsen, R.[Rasmus],
On the regularization path of the support vector domain description,
PRL(31), No. 13, 1 October 2010, pp. 1919-1923.
Elsevier DOI 1003
Support vector domain description (SVDD); Regularization path; One-class classifier; Novelty detection BibRef

Filippone, M.[Maurizio], Sanguinetti, G.[Guido],
Information theoretic novelty detection,
PR(43), No. 3, March 2010, pp. 805-814.
Elsevier DOI 1001
Novelty detection; Information theory; Mixture of Gaussians; Density estimation BibRef

Li, Y.H.[Yu-Hua],
Selecting training points for one-class support vector machines,
PRL(32), No. 11, 1 August 2011, pp. 1517-1522.
Elsevier DOI 1108
One-class support vector machines; Training set selection; Extreme points; Novelty detection BibRef

Xiao, Y.C.[Ying-Chao], Wang, H.G.[Huan-Gang], Xu, W.L.[Wen-Li], Zhou, J.[Junwu],
L1 norm based KPCA for novelty detection,
PR(46), No. 1, January 2013, pp. 389-396.
Elsevier DOI 1209
KPCA; L1 norm; Novelty detection One class classification problem BibRef

de Morsier, F., Tuia, D., Borgeaud, M., Gass, V., Thiran, J.P.,
Semi-Supervised Novelty Detection Using SVM Entire Solution Path,
GeoRS(51), No. 4, April 2013, pp. 1939-1950.
IEEE DOI 1304
BibRef

Jumutc, V., Suykens, J.A.K.[Johan A.K.],
Multi-Class Supervised Novelty Detection,
PAMI(36), No. 12, December 2014, pp. 2510-2523.
IEEE DOI 1411
Algorithm design and analysis BibRef

Rudi, A.[Alessandro], Odone, F.[Francesca], de Vito, E.[Ernesto],
Geometrical and computational aspects of Spectral Support Estimation for novelty detection,
PRL(36), No. 1, 2014, pp. 107-116.
Elsevier DOI 1312
Support estimation BibRef

Sadooghi, M.S.[Mohammad Saleh], Khadem, S.E.[Siamak Esmaeilzadeh],
Improving one class support vector machine novelty detection scheme using nonlinear features,
PR(83), 2018, pp. 14-33.
Elsevier DOI 1808
Novelty detection, OC-SVM, Nonlinear feature, Wavelet, Bearing vibration signal, Entropy BibRef

Vo, B.N.[Ba-Ngu], Dam, N.[Nhan], Phung, D.[Dinh], Tran, N.Q.[Nhat-Quang], Vo, B.T.[Ba-Tuong],
Model-based learning for point pattern data,
PR(84), 2018, pp. 136-151.
Elsevier DOI 1809
BibRef
Earlier: A1, A4, A3, A5, Only:
Model-Based Classification and Novelty Detection for Point Pattern Data,
ICPR16(2622-2627)
IEEE DOI 1705
BibRef
And: A4, A1, A3, A5, Only:
Clustering for point pattern data,
ICPR16(3174-3179)
IEEE DOI 1705
Point pattern, Point process, Random finite set, Multiple instance learning, Classification, Novelty detection, Clustering. Computational modeling, Data models, Maximum likelihood estimation, Measurement units, Niobium, Radio frequency, Training data, multiple instance data, naive Bayes model. Data models, Feature extraction, Indexes, Measurement, Clustering, affinity propagation, expectation-maximization. BibRef

Mohammadi-Ghazi, R.[Reza], Marzouk, Y.M.[Youssef M.], Büyüköztürk, O.[Oral],
Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection,
PR(81), 2018, pp. 601-614.
Elsevier DOI 1806
Novelty detection, Mixture models, Graphical models, Conditional dependence, Conditional density, False positive BibRef

Zhang, Y.Y.[Ying-Ying], Gong, Y.X.[Yu-Xin], Zhu, H.G.[Hao-Gang], Bai, X.[Xiao], Tang, W.Z.[Wen-Zhong],
Multi-head enhanced self-attention network for novelty detection,
PR(107), 2020, pp. 107486.
Elsevier DOI 2008
One-class classification, Multihead attention network, Adversarial-balance loss, Adversarial Learning, Multihead enhanced self-attention BibRef

Pang, G.S.[Guan-Song], Shen, C.H.[Chun-Hua], Cao, L.B.[Long-Bing], van den Hengel, A.J.[Anton J.],
Deep Learning for Anomaly Detection: A Review,
Surveys(54), No. 2, March 2021, pp. xx-yy.
DOI Link 2104
Anomaly detection, outlier detection, novelty detection, one-class classification, deep learning BibRef


Park, J.[Jaewoo], Jung, Y.G.[Yoon Gyo], Teoh, A.B.J.[Andrew Beng Jin],
Discriminative Multi -level Reconstruction under Compact Latent Space for One-Class Novelty Detection,
ICPR21(7095-7102)
IEEE DOI 2105
Force measurement, Semantics, Measurement uncertainty, Force, Extraterrestrial measurements, Data models BibRef

Kwon, G., Prabhushankar, M., Temel, D., Al Regib, G.,
Novelty Detection Through Model-Based Characterization of Neural Networks,
ICIP20(3179-3183)
IEEE DOI 2011
Image reconstruction, Neural networks, Training, Feature extraction, Loss measurement, Backpropagation, Decoding, Representation learning BibRef

Oza, P.[Poojan], Nguyen, H.V.[Hien V.], Patel, V.M.[Vishal M.],
Multiple Class Novelty Detection Under Data Distribution Shift,
ECCV20(VII:432-449).
Springer DOI 2011
BibRef

Oza, P.[Poojan], Patel, V.M.[Vishal M.],
Utilizing Patch-level Category Activation Patterns for Multiple Class Novelty Detection,
ECCV20(X:421-437).
Springer DOI 2011
Novel samples. BibRef

Bhattacharjee, S., Mandal, D., Biswas, S.,
Multi-class Novelty Detection Using Mix-up Technique,
WACV20(1389-1398)
IEEE DOI 2006
Training, Testing, Task analysis, Training data, Machine learning, Predictive models, Image color analysis BibRef

Abati, D.[Davide], Porrello, A.[Angelo], Calderara, S.[Simone], Cucchiara, R.[Rita],
Latent Space Autoregression for Novelty Detection,
CVPR19(481-490).
IEEE DOI 2002
BibRef

Bhattacharjee, S.[Supritam], Mudunuri, S.P.[Sivaram P.], Biswas, S.[Soma],
Do I Know You? A Two-Stage Framework for Novelty Detection,
ICIP19(2536-2540)
IEEE DOI 1910
Does the query belong to a trained class or something else. Novelty detection, comparator network, Score fusion BibRef

Bhattacharjee, S., Mandal, D., Biswas, S.,
Autoencoder based novelty detection for generalized zero shot learning,
ICIP19(3646-3650)
IEEE DOI 1910
Generalized Zero Shot Learning, Novelty Detection, Autoencoder BibRef

Perera, P.[Pramuditha], Nallapati, R.[Ramesh], Xiang, B.[Bing],
OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations,
CVPR19(2893-2901).
IEEE DOI 2002
BibRef

Lee, K.[Kibok], Lee, K.[Kimin], Min, K.[Kyle], Zhang, Y.T.[Yu-Ting], Shin, J.[Jinwoo], Lee, H.L.[Hong-Lak],
Hierarchical Novelty Detection for Visual Object Recognition,
CVPR18(1034-1042)
IEEE DOI 1812
Find closest superclass of novel object. Taxonomy, Task analysis, Cats, Training, Semantics, Object recognition BibRef

Aitchison, M., Green, R.,
Novelty Detection in Thermal Video,
IVCNZ18(1-6)
IEEE DOI 1902
Training, Neural networks, Estimation, Measurement, Birds, Rats, Manifolds, Deep Neural Networks (DNN), Novelty Detection, Density Estimation BibRef

Schultheiss, A.[Alexander], Käding, C.[Christoph], Freytag, A.[Alexander], Denzler, J.[Joachim],
Finding the Unknown: Novelty Detection with Extreme Value Signatures of Deep Neural Activations,
GCPR17(226-238).
Springer DOI 1711
Which level of CNN has extreme values. BibRef

Bodesheim, P.[Paul], Freytag, A.[Alexander], Rodner, E.[Erik], Denzler, J.[Joachim],
Local Novelty Detection in Multi-class Recognition Problems,
WACV15(813-820)
IEEE DOI 1503
Computational modeling BibRef

Bodesheim, P.[Paul], Freytag, A.[Alexander], Rodner, E.[Erik], Kemmler, M.[Michael], Denzler, J.[Joachim],
Kernel Null Space Methods for Novelty Detection,
CVPR13(3374-3381)
IEEE DOI 1309
kernel methods. Finding unknown objects. BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Human Motion Understanding and Analysis .


Last update:Jul 28, 2021 at 22:23:09