Fukunaga, K., and
Hayes, R.R.,
Effects of Sample Size in Classifier Design,
PAMI(11), No. 8, August 1989, pp. 873-885.
IEEE DOI
BibRef
8908
Raudys, S.J., and
Jain, A.K.,
Small Sample Size Effects in Statistical Pattern Recognition:
Recommendations for Practitioners,
PAMI(13), No. 3, March 1991, pp. 252-264.
IEEE DOI
BibRef
9103
Earlier:
Small sample size effects in statistical pattern recognition:
recommendations for practitioners and open problems,
ICPR90(I: 417-423).
IEEE DOI
9006
BibRef
Wong, A.K.C., and
Chiu, D.K.Y.,
Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data,
PAMI(9), No. 6, November 1987, pp. 796-805.
BibRef
8711
Chiu, D.K.Y.,
Wong, A.K.C., and
Chan, K.C.C.,
Synthesis of Statistical Knowledge from Time-Dependent Data,
PAMI(13), No. 3, March 1991, pp. 265-271.
IEEE DOI
BibRef
9103
Chan, S.C.,
Wong, A.K.C.,
Synthesis and recognition of sequences,
PAMI(13), No. 12, December 1991, pp. 1245-1255.
IEEE DOI
0401
BibRef
Ibrahim, J.,
Incomplete Data in Generalized Linear Models,
ASAJ(85), 1990, pp. 765-769.
BibRef
9000
Krishnan, T.,
Nandy, S.C.,
Efficiency of discriminant analysis when initial samples are classified
stochastically,
PR(23), No. 5, 1990, pp. 529-537.
Elsevier DOI
0401
BibRef
Benali, H.,
Buvat, I.,
Frouin, F.,
Bazin, J.P.,
di Paola, R.,
Foundations of Factor Analysis of Medical Image Sequences:
A Unified Approach and Some Practical Implications,
IVC(12), No. 6, July-August 1994, pp. 375-385.
Elsevier DOI
0401
BibRef
Lazo-Cortes, M.,
Ruiz-Shulcloper, J.,
Determining the Feature Relevance for Nonclassically Described Objects
and a New Algorithm to Compute Typical Fuzzy Testors,
PRL(16), No. 12, December 1995, pp. 1259-1265.
BibRef
9512
Lazo-Cortes, M.[Manuel],
Ruiz-Shulcloper, J.[Jose],
Alba-Cabrera, E.[Eduardo],
An Overview of the Evolution of the Concept of Testor,
PR(34), No. 4, April 2001, pp. 753-762.
Elsevier DOI
0101
BibRef
Sanchez-Díaz, G.[Guillermo],
Lazo-Cortés, M.[Manuel],
CT-EXT: An Algorithm for Computing Typical Testor Set,
CIARP07(506-514).
Springer DOI
0711
BibRef
Castelli, V.,
Cover, T.M.,
The Relative Value of Labeled and Unlabeled Samples in
Pattern-Recognition with an Unknown Mixing Parameter,
IT(42), No. 6, Part 2, November 1996, pp. 2102-2117.
9701
BibRef
Jain, A.K.[Anil K.],
Zongker, D.[Douglas],
Feature-Selection:
Evaluation, Application, and Small Sample Performance,
PAMI(19), No. 2, February 1997, pp. 153-158.
IEEE DOI
9703
BibRef
Earlier: A2, A1:
Algorithms for Feature Selection: An Evaluation,
ICPR96(II: 18-22).
IEEE DOI
9608
(Michigan State Univ., USA)
Sequential Forward Floating Selection algorithm
(
See also Floating Search Methods in Feature-Selection. )
dominates other tested algorithms. Applied with 4 texture models on SAR.
BibRef
Raudys, S.J.[Sarunas J.],
Dimensionality, Sample-Size, and Classification Error of
Nonparametric Linear Classification Algorithms,
PAMI(19), No. 6, June 1997, pp. 667-671.
IEEE DOI
9708
BibRef
Earlier:
Linear Classifiers in Perceptron Design,
ICPR96(IV: 763-767).
IEEE DOI
9608
(Institute of Mathematics and Informatics, LIT)
BibRef
Tseng, C.H.,
Identification of Cubically Nonlinear Systems Using Undersampled Data,
VISP(144), No. 5, October 1997, pp. 267-277.
9806
BibRef
Skurichina, M.[Marina],
Duin, R.P.W.[Robert P.W.],
Regularisation of Linear Classifiers by Adding Redundant Features,
PAA(2), No. 1, 1999, pp. 44-52.
BibRef
9900
Hodgson, M.E.,
What Size Window for Image Classification: A Cognitive Perspective,
PhEngRS(64), No. 8, August 1998, pp. 797-807.
9808
BibRef
Guyon, I.[Isabelle],
Makhoul, J.[John],
Schwartz, R.[Richard],
Vapnik, V.[Vladimir],
What Size Test Set Gives Good Error Rate Estimates?,
PAMI(20), No. 1, January 1998, pp. 52-64.
IEEE DOI
9803
OCR. Applied to the character recognition problem.
BibRef
Avena, G.C.,
Ricotta, C.,
Volpe, F.,
The influence of principal component analysis on the spatial structure
of a multispectral dataset,
JRS(20), No. 17, November 1999, pp. 3367.
BibRef
9911
Sitek, A.,
Gullberg, G.T.,
Huesman, R.H.,
Correction for ambiguous solutions in factor analysis using a penalized
least squares objective,
MedImg(21), No. 3, March 2002, pp. 216-225.
IEEE Top Reference.
0205
BibRef
Ennaji, A.[Abdellatif],
Ribert, A.[Arnaud],
Lecourtier, Y.[Yves],
From data topology to a modular classifier,
IJDAR(6), No. 1, 2003, pp. 1-9.
Springer DOI
0308
BibRef
Ribert, A.,
Ennaji, A.,
Lecourtier, Y.,
Clustering Data: Dealing with High Density Variations,
ICPR00(Vol II: 736-739).
IEEE DOI
0009
BibRef
Stocker, E.,
Ribert, A.,
Lecourtier, Y.,
Ennaji, A.,
Incremental Distributed Classifier Building,
ICPR96(IV: 128-132).
IEEE DOI
9608
(Univ. de Rouen, F)
BibRef
Liu, Z.Y.,
Chiu, K.C.,
Xu, L.,
Investigations on Non-Gaussian Factor Analysis,
SPLetters(11), No. 7, July 2004, pp. 597-600.
IEEE Abstract.
0407
BibRef
Wang, Y.[Ye],
Huang, S.T.[Shang-Teng],
Training TSVM with the proper number of positive samples,
PRL(26), No. 14, 15 October 2005, pp. 2187-2194.
Elsevier DOI
0510
Transductive Support Vector Machine.
BibRef
Knijnenburg, T.A.[Theo A.],
Reinders, M.J.T.[Marcel J.T.],
Wessels, L.F.A.[Lodewyk F.A.],
Artifacts of Markov blanket filtering based on discretized features in
small sample size applications,
PRL(27), No. 7, May 2006, pp. 709-714.
Elsevier DOI
0604
Feature evaluation and selection. Apply to gene expression data.
BibRef
Lai, C.[Carmen],
Reinders, M.J.T.[Marcel J.T.],
Wessels, L.F.A.[Lodewyk F.A.],
Random subspace method for multivariate feature selection,
PRL(27), No. 10, 15 July 2006, pp. 1067-1076.
Elsevier DOI
0606
Random subspace method; Small sample size problem
BibRef
Li, Y.L.[Yun-Lei],
Wessels, L.F.A.[Lodewyk F.A.],
de Ridder, D.[Dick],
Reinders, M.J.T.[Marcel J.T.],
Classification in the presence of class noise using a probabilistic
Kernel Fisher method,
PR(40), No. 12, December 2007, pp. 3349-3357.
Elsevier DOI
0709
BibRef
And: Erratum:
PR(41), No. 3, March 2008, pp. 1214.
Elsevier DOI
0711
Classification; Class noise; Labeling noise; Kernel Fisher discriminant
BibRef
Liang, Y.X.[Yi-Xiong],
Li, C.R.[Cheng-Rong],
Gong, W.G.[Wei-Guo],
Pan, Y.J.[Ying-Jun],
Uncorrelated linear discriminant analysis based on weighted pairwise
Fisher criterion,
PR(40), No. 12, December 2007, pp. 3606-3615.
Elsevier DOI
0709
Uncorrelated LDA; Null space LDA; Weighted pairwise Fisher criterion;
Decorrelation
BibRef
Isaksson, A.,
Wallman, M.,
Goransson, H.,
Gustafsson, M.G.,
Cross-validation and bootstrapping are unreliable in small sample
classification,
PRL(29), No. 14, October 2008, pp. 1960-1965.
Elsevier DOI
0804
Supervised classification; Performance estimation; Confidence interval
BibRef
Das, K.[Koel],
Nenadic, Z.[Zoran],
An efficient discriminant-based solution for small sample size problem,
PR(42), No. 5, May 2009, pp. 857-866.
Elsevier DOI
0902
Feature extraction; Principal component analysis; Classification;
Linear discriminant analysis; Bayes error
BibRef
Jacquemont, S.[Stephanie],
Jacquenet, F.[Francois],
Sebban, M.[Marc],
A lower bound on the sample size needed to perform a significant
frequent pattern mining task,
PRL(30), No. 11, 1 August 2009, pp. 960-967.
Elsevier DOI
0909
Frequent pattern mining; Lower bound
BibRef
Macià, N.[Núria],
Bernadó-Mansilla, E.[Ester],
Orriols-Puig, A.[Albert],
Ho, T.K.[Tin Kam],
Learner excellence biased by data set selection:
A case for data characterisation and artificial data sets,
PR(46), No. 3, March 2013, pp. 1054-1066.
Elsevier DOI
1212
BibRef
Earlier: A1, A2, A3:
Preliminary approach on synthetic data sets generation based on class
separability measure,
ICPR08(1-4).
IEEE DOI
0812
Supervised learning; Learner assessment; Data complexity
BibRef
Macià, N.[Núria],
Ho, T.K.[Tin Kam],
Orriols-Puig, A.[Albert],
Bernadó-Mansilla, E.[Ester],
The Landscape Contest at ICPR 2010,
ICPR-Contests10(29-45).
Springer DOI
1008
Evaluate robustness of supervised classifications and their limitations.
BibRef
Hanczar, B.[Blaise],
Dougherty, E.R.[Edward R.],
The reliability of estimated confidence intervals for classification
error rates when only a single sample is available,
PR(46), No. 3, March 2013, pp. 1067-1077.
Elsevier DOI
1212
Supervised learning; Error estimation; High dimension; Small sample
setting; Confidence interval
BibRef
Wu, H.[Hao],
Miao, Z.J.[Zhen-Jiang],
Wang, Y.[Yi],
Lin, M.[Manna],
Optimized recognition with few instances based on semantic distance,
VC(31), No. 4, April 2015, pp. 367-375.
Springer DOI
1503
Learning with only a few examples.
BibRef
Hsiao, P.H.[Pai-Heng],
Chang, F.J.[Feng-Ju],
Lin, Y.Y.[Yen-Yu],
Learning Discriminatively Reconstructed Source Data for Object
Recognition With Few Examples,
IP(25), No. 8, August 2016, pp. 3518-3532.
IEEE DOI
1608
learning systems
BibRef
Potapov, A.[Alexey],
Potapova, V.[Vita],
Peterson, M.[Maxim],
A feasibility study of an autoencoder meta-model for improving
generalization capabilities on training sets of small sizes,
PRL(80), No. 1, 2016, pp. 24-29.
Elsevier DOI
1609
Autoencoders
BibRef
Li, D.[Dong],
Liu, S.L.[Shu-Lin],
Zhang, H.L.[Hong-Li],
A method of anomaly detection and fault diagnosis with online
adaptive learning under small training samples,
PR(64), No. 1, 2017, pp. 374-385.
Elsevier DOI
1701
Artificial immune system
BibRef
Kuncheva, L.I.[Ludmila I.],
Rodríguez, J.J.[Juan J.],
On feature selection protocols for very low-sample-size data,
PR(81), 2018, pp. 660-673.
Elsevier DOI
1806
Feature selection, Wide datasets, Experimental protocol,
Training/testing, Cross-validation
BibRef
Pan, B.[Bin],
Shi, Z.W.[Zhen-Wei],
Xu, X.[Xia],
MugNet: Deep Learning for Hyperspectral Image Classification Using
Limited Samples,
PandRS(145), 2018, pp. 108-119.
Elsevier DOI
1810
Hyperspectral image classification, Deep learning,
Multi-grained scanning, MugNet
BibRef
Cadoni, M.[Marinella],
Lagorio, A.[Andrea],
Grosso, E.[Enrico],
Incremental models based on features persistence for object
recognition,
PRL(122), 2019, pp. 38-44.
Elsevier DOI
1904
Limited number of examples.
View-based object recognition, Incremental object models, Features persistence
BibRef
Wei, W.[Wei],
Dai, H.[Hua],
Liang, W.T.[Wei-Tai],
Exponential sparsity preserving projection with applications to image
recognition,
PR(104), 2020, pp. 107357.
Elsevier DOI
2005
Sparsity preserving projection, Dimensionality reduction,
Small-sample-size problem, Matrix exponential, Image recognition
BibRef
Zhai, Y.K.[Yi-Kui],
Deng, W.[Wenbo],
Lan, T.[Tian],
Sun, B.[Bing],
Ying, Z.[Zilu],
Gan, J.Y.[Jun-Ying],
Mai, C.Y.[Chao-Yun],
Li, J.W.[Jing-Wen],
Labati, R.D.[Ruggero Donida],
Piuri, V.[Vincenzo],
Scotti, F.[Fabio],
MFFA-SARNET: Deep Transferred Multi-Level Feature Fusion Attention
Network with Dual Optimized Loss for Small-Sample SAR ATR,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link
2005
BibRef
Wen, Z.D.[Zai-Dao],
Liu, Z.G.[Zhun-Ga],
Zhang, S.[Shuai],
Pan, Q.[Quan],
Rotation Awareness Based Self-Supervised Learning for SAR Target
Recognition With Limited Training Samples,
IP(30), 2021, pp. 7266-7279.
IEEE DOI
2108
Target recognition, Task analysis, Training,
Synthetic aperture radar, Azimuth, Sensitivity, Feature extraction,
automatic target recognition
BibRef
Feng, F.[Fan],
Zhang, Y.S.[Yong-Sheng],
Zhang, J.[Jin],
Liu, B.[Bing],
Small Sample Hyperspectral Image Classification Based on Cascade
Fusion of Mixed Spatial-Spectral Features and Second-Order Pooling,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Lin, L.[Luyue],
Liu, B.[Bo],
Zheng, X.[Xin],
Xiao, Y.[Yanshan],
An Efficient Image Categorization Method With Insufficient Training
Samples,
Cyber(52), No. 5, May 2022, pp. 3244-3260.
IEEE DOI
2206
Training, Decoding, Neural networks, Task analysis,
Learning systems, variational autoencoder (VAE)
BibRef
Ding, C.[Chen],
Chen, Y.[Youfa],
Li, R.[Runze],
Wen, D.[Dushi],
Xie, X.Y.[Xiao-Yan],
Zhang, L.[Lei],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Integrating Hybrid Pyramid Feature Fusion and Coordinate Attention
for Effective Small Sample Hyperspectral Image Classification,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Zhang, S.H.[Shu-Han],
Zhang, X.H.[Xiao-Hua],
Li, T.R.[Tian-Rui],
Meng, H.Y.[Hong-Yun],
Cao, X.H.[Xiang-Hai],
Wang, L.[Li],
Adversarial Representation Learning for Hyperspectral Image
Classification with Small-Sized Labeled Set,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Cao, X.F.[Xiao-Feng],
Tsang, I.W.[Ivor W.],
Distribution Disagreement via Lorentzian Focal Representation,
PAMI(44), No. 10, October 2022, pp. 6872-6889.
IEEE DOI
2209
Annotations, Neural networks, Training, Geometry, Deep learning,
Data models, Complexity theory, Error disagreement,
hyperbolic geometry
BibRef
Feng, F.[Fan],
Zhang, Y.S.[Yong-Sheng],
Zhang, J.[Jin],
Liu, B.[Bing],
Low-Rank Constrained Attention-Enhanced Multiple
Spatial-Spectral Feature Fusion for Small Sample Hyperspectral
Image Classification,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Jeon, H.[Hyeonseong],
Han, S.[Siho],
Lee, S.[Sangwon],
Woo, S.S.[Simon S.],
Compensating for the Lack of Extra Training Data by Learning Extra
Representation,
ACCV20(VI:532-548).
Springer DOI
2103
BibRef
Hurtik, P.[Petr],
Molek, V.[Vojtech],
Perfilieva, I.[Irina],
Novel dimensionality reduction approach for unsupervised learning on
small datasets,
PR(103), 2020, pp. 107291.
Elsevier DOI
2005
Unsupervised learning, Dimensionality reduction, PCA,
F-transform, Image classification, Autoencoder
BibRef
Barz, B.,
Denzler, J.,
Deep Learning on Small Datasets without Pre-Training using Cosine
Loss,
WACV20(1360-1369)
IEEE DOI
2006
Machine learning, Training, Semantics, Face, Task analysis,
Neural networks, Training data
BibRef
Sandler, M.,
Baccash, J.,
Zhmoginov, A.,
Howard, A.,
Non-Discriminative Data or Weak Model? On the Relative Importance of
Data and Model Resolution,
RLQ19(1036-1044)
IEEE DOI
2004
image resolution, neural net architecture,
isometric neural networks, fixed internal resolution,
BibRef
Kaushal, V.,
Iyer, R.,
Kothawade, S.,
Mahadev, R.,
Doctor, K.,
Ramakrishnan, G.,
Learning From Less Data: A Unified Data Subset Selection and Active
Learning Framework for Computer Vision,
WACV19(1289-1299)
IEEE DOI
1904
learning (artificial intelligence), neural nets,
object detection, object recognition,
Data models
BibRef
Zhang, Z.L.[Zong-Liang],
Li, J.[Jonathan],
Guo, Y.L.[Yu-Lan],
Li, X.[Xin],
Lin, Y.B.[Yang-Bin],
Xiao, G.B.[Guo-Bao],
Wang, C.[Cheng],
Robust procedural model fitting with a new geometric similarity
estimator,
PR(85), 2019, pp. 120-131.
Elsevier DOI
1810
Complex model fitting, Imperfect point set,
Inverse procedural modeling, Probabilistic program induction,
Few-shot pattern recognition
BibRef
Davari, A.[Amir_Abbas],
Christlein, V.[Vincent],
Vesal, S.[Sulaiman],
Maier, A.[Andreas],
Riess, C.[Christian],
GMM Supervectors for Limited Training Data in Hyperspectral Remote
Sensing Image Classification,
CAIP17(II: 296-306).
Springer DOI
1708
BibRef
Ustuner, M.,
Sanli, F.B.,
Abdikan, S.,
Balanced Vs Imbalanced Training Data: Classifying Rapideye Data With
Support Vector Machines,
ISPRS16(B7: 379-384).
DOI Link
1610
BibRef
Su, H.[Hang],
Yin, Z.Z.[Zhao-Zheng],
Kanade, T.[Takeo],
Huh, S.[Seungil],
Active sample selection and correction propagation on a
gradually-augmented graph,
CVPR15(1975-1983)
IEEE DOI
1510
BibRef
Kadar, I.[Ilan],
Ben-Shahar, O.[Ohad],
Small sample scene categorization from perceptual relations,
CVPR12(2711-2718).
IEEE DOI
1208
BibRef
Singh, M.[Mayank],
Gupta, P.K.,
Mishra, S.[Shailendra],
Automated test data generation for mutation testing using AspectJ
programs,
ICIIP11(1-5).
IEEE DOI
1112
BibRef
Xia, X.T.[Xin-Tao],
Zhou, Q.[Qing],
Zhu, J.M.[Jian-Min],
Evaluation for repeatability and reproducibility of information poor
process,
IASP10(528-531).
IEEE DOI
1004
Incomplete, small sample size.
BibRef
Petersen, H.[Henry],
Poon, J.[Josiah],
Reworking Bridging for Use within the Image Domain,
CAIP09(832-839).
Springer DOI
0909
Bridging: from string text classification (Zelikovitz et al.)
Data set issues.
BibRef
Ricamato, M.T.[Maria Teresa],
Marrocco, C.[Claudio],
Tortorella, F.[Francesco],
MCS-based balancing techniques for skewed classes:
An empirical comparison,
ICPR08(1-4).
IEEE DOI
0812
Train multiple classifiers using the minority class and part of majority class.
BibRef
Na, J.H.[Jin Hee],
Yun, S.M.[Seok Min],
Kim, M.S.[Min-Soo],
Choi, J.Y.[Jin Young],
Relevant pattern selection for subspace learning,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Xu, Y.[Yong],
Zhang, D.[David],
A New Solution Scheme of Unsupervised Locality Preserving Projection
Method for the SSS Problem,
SSPR08(775-781).
Springer DOI
0812
Small Sample Size problem.
BibRef
Morales-Manilla, L.R.[Luis Roberto],
Sanchez-Diaz, G.[Guillermo],
FS-EX Plus: A New Algorithm for the Calculation of Typical FS-Testor
Set,
CIARP07(380-386).
Springer DOI
0711
Feature Selection.
BibRef
Zheng, Y.J.[Yu-Jie],
Yang, J.Y.[Jing-Yu],
Yang, J.[Jian],
Wu, X.J.[Xiao-Jun],
Effective classification image space which can solve small sample size
problem,
ICPR06(II: 861-864).
IEEE DOI
0609
BibRef
Pranckeviciene, E.[Erinija],
Ho, T.K.[Tin Kam],
Somorjai, R.[Ray],
Class Separability in Spaces Reduced By Feature Selection,
ICPR06(III: 254-257).
IEEE DOI
0609
BibRef
Xuan, G.R.[Guo-Rong],
Zhu, X.M.[Xiu-Ming],
Chai, P.Q.[Pei-Qi],
Zhang, Z.P.[Zhen-Ping],
Shi, Y.Q.[Yun Q.],
Fu, D.D.[Dong-Dong],
Feature Selection based on the Bhattacharyya Distance,
ICPR06(III: 1232-1235).
IEEE DOI
0609
BibRef
And:
ICPR06(IV: 957).
IEEE DOI
0609
BibRef
Levi, K.[Kobi],
Fink, M.[Michael],
Weiss, Y.[Yair],
Learning From a Small Number of Training Examples by Exploiting Object
Categories,
LCV04(96).
IEEE DOI
0406
BibRef
Ulusoy, I.[Ilkay],
Bishop, C.M.[Christopher M.],
Comparison of Generative and Discriminative Techniques for Object
Detection and Classification,
CLOR06(173-195).
Springer DOI
0711
BibRef
Earlier:
Generative versus Discriminative Methods for Object Recognition,
CVPR05(II: 258-265).
IEEE DOI
0507
BibRef
Salah, A.A.,
Alpaydin, E.,
Incremental mixtures of factor analysers,
ICPR04(I: 276-279).
IEEE DOI
0409
BibRef
Huang, R.[Rui],
Liu, Q.S.[Qing-Shan],
Lu, H.Q.[Han-Qing],
Ma, S.D.[Song-De],
Solving the small sample size problem of LDAf,
ICPR02(III: 29-32).
IEEE DOI
0211
BibRef
Duin, R.P.W.[Robert P.W.],
Relational Discriminant Analysis and its Large Sample Size Problem,
ICPR98(Vol I: 445-449).
IEEE DOI
9808
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Unbalanced Datasets, Imbalanced Sample Sizes, Imbalanced Data, Long-Tailed Data .