Random Projection RBF Nets for Multidimensional Density Estimation
From MaRDI portal
Publication:3601357
DOI10.2478/v10006-008-0040-9zbMath1155.93428OpenAlexW2161722909MaRDI QIDQ3601357
Publication date: 10 February 2009
Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)
Full work available at URL: https://eudml.org/doc/207899
dimension reductionradial basis functionsmultivariate density estimationnovelty detectionnormal random projection
Control/observation systems involving computers (process control, etc.) (93C83) Estimation and detection in stochastic control theory (93E10) Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70)
Related Items
Sampling multidimensional signals by a new class of quasi-random sequences ⋮ Estimation of horizontal and vertical translations of large images based on columns and rows mean energy matching ⋮ Random projections and Hotelling’s T2 statistics for change detection in high-dimensional data streams
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The Johnson-Lindenstrauss lemma and the sphericity of some graphs
- A survey of design methods for failure detection in dynamic systems
- On radial basis function nets and kernel regression: Statistical consistency, convergence rates, and receptive field size
- Convergence and rates of convergence of radial basis functions networks in function learning.
- An algorithmic theory of learning: Robust concepts and random projection
- Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
- Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
- A Brief Survey of Bandwidth Selection for Density Estimation
- Extensions of Lipschitz mappings into a Hilbert space
- A study on neuro-fuzzy systems for fault diagnosis
- Nearest-neighbor-preserving embeddings
- Radial Basis Functions
- Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur
- Artificial Intelligence and Soft Computing - ICAISC 2004
- An elementary proof of a theorem of Johnson and Lindenstrauss
- On Estimation of a Probability Density Function and Mode