14.1.4.1 Sample Sizes Issues, Data analysis, Training Sets

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
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

Zongker, D., Jain, A.K.,
Algorithms for Feature Selection: An Evaluation,
ICPR96(II: 18-22).
IEEE DOI 9608
(Michigan State Univ., USA) 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

Hernandez-Leal, P.[Pablo], Carrasco-Ochoa, J.A.[J. Ariel], Martínez-Trinidad, J.F.[José Francisco], Olvera-Lopez, J.A.[J. Arturo],
InstanceRank based on borders for instance selection,
PR(46), No. 1, January 2013, pp. 365-375.
Elsevier DOI 1209
Instance selection; Instance ranking; Border instances; Supervised classification BibRef

Olvera-López, J.A.[J. Arturo], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[J. Ariel],
Mixed Data Object Selection Based on Clustering and Border Objects,
CIARP07(674-683).
Springer DOI 0711
Instance selection. BibRef

Hernandez-Rodriguez, S.[Selene], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[J. Ariel],
On the selection of base prototypes for LAESA and TLAESA classifiers,
ICPR08(1-4).
IEEE DOI 0812
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

Jiang, Y.G.[Yu-Gang], Wang, J.[Jun], Xue, X., Chang, S.F.[Shih-Fu],
Query-Adaptive Image Search With Hash Codes,
MultMed(15), No. 2, 2013, pp. 442-453.
IEEE DOI 1302
BibRef

Jiang, Y.G.[Yu-Gang], Wang, J.[Jun], Chang, S.F.[Shih-Fu],
Lost in binarization: query-adaptive ranking for similar image search with compact codes,
ICMR11(16).
DOI Link 1301
BibRef
And: A2, A1, A3:
Label diagnosis through self tuning for web image search,
CVPR09(1390-1397).
IEEE DOI 0906
Are the initial label good? 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

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
computer vision, 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

Zhu, X.Q.[Xing-Quan], Wu, X.D.[Xin-Dong],
Scalable Representative Instance Selection and Ranking,
ICPR06(III: 352-355).
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 .