An Evaluation of Intrinsic Dimensionality Estimators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Intrinsic Dimension Estimation of Data: An Approach Based on Grassberger–Procaccia's Algorithm
Neural Processing Letters
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing Edge Models via Local Principal Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Gabor Wavelet Networks for Object Representation
Proceedings of the 10th International Workshop on Theoretical Foundations of Computer Vision: Multi-Image Analysis
Distance-Preserving Projection of High-Dimensional Data for Nonlinear Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating manifold dimension by inversion error
Proceedings of the 2005 ACM symposium on Applied computing
Crystallization sonification of high-dimensional datasets
ACM Transactions on Applied Perception (TAP)
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Magnification Control in Self-Organizing Maps and Neural Gas
Neural Computation
Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Intrinsic dimension estimation of manifolds by incising balls
Pattern Recognition
Continuous dimensionality characterization of image structures
Image and Vision Computing
A new approach to discover interlacing data structures in high-dimensional space
Journal of Intelligent Information Systems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
Learning-based robot vision: principles and applications
Learning-based robot vision: principles and applications
Mining interlacing manifolds in high dimensional spaces
Proceedings of the 2011 ACM Symposium on Applied Computing
Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA
Pattern Recognition Letters
Signal subspace identification in hyperspectral linear mixtures
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Image Dimensionality Reduction Based on the Intrinsic Dimension and Parallel Genetic Algorithm
International Journal of Cognitive Informatics and Natural Intelligence
Image Retrieval based on HSV Feature and Regional Shannon Entropy
International Journal of Software Science and Computational Intelligence
Intrinsic dimension estimation via nearest constrained subspace classifier
Pattern Recognition
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A new method for analyzing the intrinsic dimensionality (ID) of low-dimensional manifolds in high-dimensional feature spaces is presented. Compared to a previous approach by Fukunaga and Olsen, the method has only linear instead of cubic time complexity w.r.t. the dimensionality of the input space. Moreover, it is less sensitive to noise than the former approach. Experiments include ID estimation of synthetic data for comparison and illustration as well as ID estimation of an image sequence.