Dimension reduction by local principal component analysis
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Self-Organizing Maps
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Neural networks in financial engineering: a study in methodology
IEEE Transactions on Neural Networks
Methodology for long-term prediction of time series
Neurocomputing
An evaluation of dimension reduction techniques for one-class classification
Artificial Intelligence Review
Subspace sums for extracting non-random data from massive noise
Knowledge and Information Systems
RCGA-S/RCGA-SP Methods to Minimize the Delta Test for Regression Tasks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
EDA-Based Logistic Regression Applied to Biomarkers Selection in Breast Cancer
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Advantages of using feature selection techniques on steganalysis schemes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Point-distribution algorithm for mining vector-item patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
On the impact of the metrics choice in SOM learning: some empirical results from financial data
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Reducing the search space in evolutive design of ARIMA and ANN models for time series prediction
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Distributed learning with data reduction
Transactions on computational collective intelligence IV
Multistart strategy using delta test for variable selection
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Quality of similarity rankings in time series
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Long memory time series forecasting by using genetic programming
Genetic Programming and Evolvable Machines
On the Curse of Dimensionality in Supervised Learning of Smooth Regression Functions
Neural Processing Letters
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
Statistics and Computing
Pareto-optimal noise and approximation properties of RBF networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
An algorithm for sample and data dimensionality reduction using fast simulated annealing
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Clustering high dimensional data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A survey on unsupervised outlier detection in high-dimensional numerical data
Statistical Analysis and Data Mining
Rectifying the representation learned by Non-negative Matrix Factorization
International Journal of Knowledge-based and Intelligent Engineering Systems
Hybrid random subsample classifier ensemble for high dimensional data sets
International Journal of Hybrid Intelligent Systems
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Modern data analysis tools have to work on high-dimensional data, whose components are not independently distributed. High-dimensional spaces show surprising, counter-intuitive geometrical properties that have a large influence on the performances of data analysis tools. Among these properties, the concentration of the norm phenomenon results in the fact that Euclidean norms and Gaussian kernels, both commonly used in models, become inappropriate in high-dimensional spaces. This papers presents alternative distance measures and kernels, together with geometrical methods to decrease the dimension of the space. The methodology is applied to a typical time series prediction example.