14.1.3.5.3 Intrinsic Dimensionality

Chapter Contents (Back)
Dimensionality. See also Number of Features, Dimensionality Reduction.

Fukunaga, K., and Olsen, D.R.,
An Algorithm for Finding Intrinsic Dimensionality of Data,
TC(20), No. 2, 1971, pp. 176-183. Cubic time complexity with respect to dimensionality of input space. See also Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering. BibRef 7100

Koontz, W.L.G., and Fukunaga, K.,
A Nonparametric Valley-Seeking Technique for Cluster Analysis,
TC(21), 1972, pp. 171-178. BibRef 7200

Koontz, W.L.G., and Fukunaga, K.,
Asymptotic Analysis of a Nonparametric Clustering Technique,
TC(21), 1972, pp. 967-974. BibRef 7200

Koontz, W.L.G., Narendra, P.M., and Fukunaga, K.,
A Graph-Theoretic Approach to Nonparametric Cluster Analysis,
TC(25), 1976, pp. 936-944. BibRef 7600

Raudys, S.J.[Sarunas J.],
Determination of optimal dimensionality in statistical pattern classification,
PR(11), No. 4, 1979, pp. 263-270.
Elsevier DOI 0309
BibRef

Raudys, S.J.[Sarunas J.], Pikelis, V.,
On Dimensionality, Sample Size, Classification Error, and Complexity of Classification Algorithms in Pattern Recognition,
PAMI(2), No. 3, May 1980, pp. 243-252. Dimensionality analysis. BibRef 8005

Raudys, S.J.[Sarunas J.],
Feature Over-Selection,
SSPR06(622-631).
Springer DOI 0608
BibRef

Schwartz, G.,
Estimating the Dimension of a Model,
AMS(6), 1978, pp. 461-464. Bayesian information criterion. BibRef 7800

Jain, A.K., Waller, W.G.,
On the optimal number of features in the classification of multivariate Gaussian data,
PR(10), No. 5-6, 1978, pp. 365-374.
Elsevier DOI 0309
BibRef

Trunk, G.V.,
Range Resolution of Targets using Automatic Detection,
AeroSys(14), No. 5, 1978, pp. 750-755. BibRef 7800

Trunk, G.V.,
A Problem of Dimensionality: A Simple Example,
PAMI(1), No. 3, July 1979, 306-307. BibRef 7907

Verveer, P.J., Duin, R.P.W.,
An Evaluation of Intrinsic Dimensionality Estimators,
PAMI(17), No. 1, January 1995, pp. 81-86.
IEEE DOI BibRef 9501

Bruske, J., Sommer, G.,
Intrinsic Dimensionality Estimation with Optimally Topology Preserving Maps,
PAMI(20), No. 5, May 1998, pp. 572-575.
IEEE DOI 9806
BibRef
Earlier:
An algorithm for intrinsic dimensionality estimation,
CAIP97(9-16).
Springer DOI 9709
Linear time complexity. BibRef

Camastra, F.[Francesco], Vinciarelli, A.[Alessandro],
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method,
PAMI(24), No. 10, October 2002, pp. 1404-1407.
IEEE Abstract. 0210
BibRef

Camastra, F.[Francesco],
Data dimensionality estimation methods: a survey,
PR(36), No. 12, December 2003, pp. 2945-2954.
Elsevier DOI 0310
Survey, Dimensionality. BibRef

Fan, M.Y.[Ming-Yu], Qiao, H.[Hong], Zhang, B.[Bo],
Intrinsic dimension estimation of manifolds by incising balls,
PR(42), No. 5, May 2009, pp. 780-787.
Elsevier DOI 0902
Nonlinear dimensionality reduction; Manifold learning; Intrinsic dimension estimation; Data mining BibRef

Fan, M.Y.[Ming-Yu], Gu, N.[Nannan], Qiao, H.[Hong], Zhang, B.[Bo],
Sparse regularization for semi-supervised classification,
PR(44), No. 8, August 2011, pp. 1777-1784.
Elsevier DOI 1104
Regularization theory; Semi-supervised learning; Regularized least square classification; Dimensionality reduction BibRef

Xie, L.X.[Ling-Xi], Tian, Q.[Qi], Wang, M.[Meng], Zhang, B.[Bo],
Spatial Pooling of Heterogeneous Features for Image Classification,
IP(23), No. 5, May 2014, pp. 1994-2008.
IEEE DOI 1405
feature extraction See also Fine-Grained Image Search. See also Heterogeneous Graph Propagation for Large-Scale Web Image Search. BibRef

Xie, L.X.[Ling-Xi], Tian, Q.[Qi], Zhang, B.[Bo],
Simple Techniques Make Sense: Feature Pooling and Normalization for Image Classification,
CirSysVideo(26), No. 7, July 2016, pp. 1251-1264.
IEEE DOI 1608
computer vision BibRef

Felsberg, M.[Michael], Kalkan, S.[Sinan], Kruger, N.[Norbert],
Continuous dimensionality characterization of image structures,
IVC(27), No. 6, 4 May 2009, pp. 628-636.
Elsevier DOI 0904
Intrinsic dimensionality; Feature extraction and classification BibRef

Bouveyron, C.[Charles], Celeux, G.[Gilles], Girard, S.C.[Stéphane C.],
Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA,
PRL(32), No. 14, 15 October 2011, pp. 1706-1713.
Elsevier DOI 1110
Probabilistic PCA; Isotropic model; Dimension reduction; Intrinsic dimension; Maximum likelihood; Asymptotic consistency BibRef

Ceruti, C.[Claudio], Bassis, S.[Simone], Rozza, A.[Alessandro], Lombardi, G.[Gabriele], Casiraghi, E.[Elena], Campadelli, P.[Paola],
DANCo: An intrinsic dimensionality estimator exploiting angle and norm concentration,
PR(47), No. 8, 2014, pp. 2569-2581.
Elsevier DOI 1405
Intrinsic dimensionality estimation BibRef

Rozza, A.[Alessandro], Lombardi, G.[Gabriele], Rosa, M.[Marco], Casiraghi, E.[Elena], Campadelli, P.[Paola],
IDEA: Intrinsic Dimension Estimation Algorithm,
CIAP11(I: 433-442).
Springer DOI 1109
Dimensionality reduction for high dimensional data BibRef

Golay, J.[Jean], Kanevski, M.[Mikhail],
A new estimator of intrinsic dimension based on the multipoint Morisita index,
PR(48), No. 12, 2015, pp. 4070-4081.
Elsevier DOI 1509
Intrinsic dimension BibRef

Liu, Y.L.[Ying-Lu], Zhang, Y.M.[Yan-Ming], Zhang, X.Y.[Xu-Yao], Liu, C.L.[Cheng-Lin],
Adaptive spatial pooling for image classification,
PR(55), No. 1, 2016, pp. 58-67.
Elsevier DOI 1604
Weighted pooling BibRef


Romano, S., Chelly, O., Nguyen, V., Bailey, J., Houle, M.E.,
Measuring dependency via intrinsic dimensionality,
ICPR16(1207-1212)
IEEE DOI 1705
Correlation, Current measurement, Entropy, Manifolds, Maximum likelihood estimation, Microwave integrated circuits, Pattern, recognition BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Discriminant Analysis .


Last update:Nov 12, 2018 at 11:26:54