Time Series Segmentation Using a Novel Adaptive Eigendecomposition Algorithm

Authors:
Yadunandana N. Rao;Jose C. Principe
Affiliations:
Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA;Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
Venue:
Journal of VLSI Signal Processing Systems
Year:
2002

Citing 13
Cited 2

Adaptive filter theory

Adaptive filter theory
Adaptive algorithms and stochastic approximations

Adaptive algorithms and stochastic approximations
Detection of abrupt changes: theory and application

Detection of abrupt changes: theory and application
Original Contribution: Least mean square error reconstruction principle for self-organizing neural-nets

Neural Networks
Principal component neural networks: theory and applications

Principal component neural networks: theory and applications
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives

Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives
On the Use of Neural Networks in the Generalized Likelihood Ratio Test for Detecting Abrupt Changes in Signals

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Gated competitive systems for unsupervised segmentation and modeling of piecewise stationary signals

Gated competitive systems for unsupervised segmentation and modeling of piecewise stationary signals
Competitive principal component analysis for locally stationarytime series

IEEE Transactions on Signal Processing
Self-organizing algorithms for generalized eigen-decomposition

IEEE Transactions on Neural Networks
Algorithms for accelerated convergence of adaptive PCA

IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection

IEEE Transactions on Neural Networks

Fast RLS-Like Algorithm for Generalized Eigendecomposition and its Applications

Journal of VLSI Signal Processing Systems
Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a new technique for time series segmentation built around a fast principal component analysis (PCA) algorithm that is on-line and stable. The traditional Generalized Likelihood Ratio Test (GLRT) has been used to solve the segmentation problem, but this has enormous limitations in terms of complexity and speed. Newer methods use gated experts and mixture models to detect transitions in time series. These techniques perform better than GLRT, but most of them require extensive training of relatively large neural networks. The segmentation method discussed in this paper is based on a novel idea that involves solving the generalized eigendecomposition of two consecutive windowed time series and can be formulated as a two-step PCA. Thus, the performance of our segmentation technique mainly depends on the efficiency of the PCA algorithm. Most of the existing techniques for PCA are based on gradient search procedures that are slow and they also suffer from convergence problems. The PCA algorithm presented in this paper is both online, and is proven to converge faster than the current methods.