Algorithms for clustering data
Algorithms for clustering data
Topology representing networks
Neural Networks
Understanding nonlinear dynamics
Understanding nonlinear dynamics
The nature of statistical learning theory
The nature of statistical learning theory
Intrinsic Dimensionality Estimation With Optimally Topology Preserving Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach to dimensionality reduction: theory and algorithms
SIAM Journal on Applied Mathematics
Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns
Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Limitations of nonlinear PCA as performed with generic neural networks
IEEE Transactions on Neural Networks
Principal components analysis competitive learning
Neural Computation
Estimating manifold dimension by inversion error
Proceedings of the 2005 ACM symposium on Applied computing
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of the optimal parameter value for the Isomap algorithm
Pattern Recognition Letters
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
REDUS: finding reducible subspaces in high dimensional data
Proceedings of the 17th ACM conference on Information and knowledge management
Intrinsic dimension estimation of manifolds by incising balls
Pattern Recognition
A new approach to discover interlacing data structures in high-dimensional space
Journal of Intelligent Information Systems
Data reduction in headspace analysis of blood and urine samples for robust bacterial identification
Computer Methods and Programs in Biomedicine
Adapting indexing trees to data distribution in feature spaces
Computer Vision and Image Understanding
Batch linear manifold topographic map with regional dimensionality estimation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
On local intrinsic dimension estimation and its applications
IEEE Transactions on Signal Processing
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Non-manifold surface reconstruction from high-dimensional point cloud data
Computational Geometry: Theory and Applications
Minimum neighbor distance estimators of intrinsic dimension
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA
Pattern Recognition Letters
IDEA: intrinsic dimension estimation algorithm
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Estimating fractal intrinsic dimension from the neighborhood
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Adaptively incremental self-organizing isometric embedding
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
SRF: a framework for the study of classifier behavior under training set mislabeling noise
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Intrinsic dimension estimation via nearest constrained subspace classifier
Pattern Recognition
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In this paper, the problem of estimating the intrinsic dimension of a data set is investigated. A fractal-based approach using the Grassberger-Procaccia algorithm is proposed. Since the Grassberger-Procaccia algorithm performs badly on sets of high dimensionality, an empirical procedure that improves the original algorithm has been developed. The procedure has been tested on data sets of known dimensionality and on time series of Santa Fe competition.