Time series classification using Gaussian mixture models of reconstructed phase spaces

Povinelli, Richard J.; Johnson, Michael T.; Lindgren, A.C.; Ye, Jinjin

doi:10.1109/tkde.2004.17

Cited by 143 publications

(74 citation statements)

References 18 publications

(22 reference statements)

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“…In practice, the changes of system dynamics are not always abrupt but vague under most practical conditions, so the fuzzy clustering technique is implemented. The close relationships between fuzzy clustering techniques and probability models also indicate that it is possible to adopt probability models to modify fuzzy approaches [34]. Therefore, the probability models are also employed in this study.…”

Section: Improved Gg Clustering-based Time Series Segmentationmentioning

confidence: 93%

“…Therefore, the union of current sections is expressed as the union is then served as the sampling pool for OS-ELM, and the length of Q(t) is denoted as L Q . At t-th step, after the segmentation is processed and the union is determined according to (34), OS-ELM is implemented for identification and prediction based on the samples in Q(t). The IGG method is conducted sequentially in this study.…”

Section: Igg-based Sampling Pool Selection For Os-elmmentioning

confidence: 99%

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An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation

Yin¹,

Wang²

2015

Proceedings in Adaptation, Learning and Optimization

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a b s t r a c tA novel sampling pool selection scheme is proposed for the online sequential extreme learning machine (OS-ELM) based on improved Gath-Geva (IGG) fuzzy segmentation algorithm. Tidal change is a timevarying process whose dynamics vary with changes of internal and environmental factors such as celestial bodies movements, coastal topology and environmental disturbances. When OS-ELM is implemented for identifying time-varying system dynamics, it usually sequentially selects samples with fixed number. Under such circumstance, samples representing different system dynamics are mixed together so that the online representation and prediction abilities of OS-ELM may be deteriorated. To consciously select samples with most representing ability and construct appropriate sampling pool for OS-ELM, in this study, a dynamic sampling pool selection scheme is proposed based on IGG fuzzy segmentation approach. Time series of input and output variables are segmented as per their dynamics characteristics. The change points split up the time series into several segments and the change points themselves represent the changes of system dynamics. Samples within the same segment are considered as possessing homogeneous characteristics. To achieve best representing abilities for current system dynamics, the proposed IGG-based sampling scheme is implemented for selecting sampling pool. The OS-ELM selects homogeneous samples from sampling pool thus possesses better representing ability for current dynamics. In the meantime, conventional harmonic analysis is also applied to represent the influences of celestial bodies and coastal topology. The harmonic method and IGG-based OS-ELM are combined together and the resulted modular prediction scheme is applied for online tidal level prediction of ports of King Point, Mokuoloe and Old Port Tampa in the United States. Simulation results demonstrate the feasibility and effectiveness of the proposed sampling scheme and the modular tidal prediction approach.

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Section: Improved Gg Clustering-based Time Series Segmentationmentioning

confidence: 93%

Section: Igg-based Sampling Pool Selection For Os-elmmentioning

confidence: 99%

An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation

Yin¹,

Wang²

2015

Proceedings in Adaptation, Learning and Optimization

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“…The dimension and lag for the RPS are determined using heuristic methods based on the false nearest neighbors and the first minimum of the automutual information. See [1] for more details on this process.…”

Section: Methodsmentioning

confidence: 99%

“…The first is a dynamical systems approach based on reconstructed phase spaces (RPSs) [1], and the second is based on the Karhunen Lòeve Transform (KLT) of the ST segment, which is provided with the LTST dataset. A Bayesian classifier over a Gaussian Mixture Model (GMM) of these features is the machine learner for creating an automatic classifier of the events.…”

Section: Introductionmentioning

confidence: 99%

Towards the prediction of transient ST changes

Povinelli

2005

Computers in Cardiology, 2005

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“…Phase space reconstruction is one of the analysis tools that has been used for nonlinear speech signal analysis [14]. Phase space reconstruction is based in dynamical systems theory, and can be used as a tool for estimating the dynamical invariants of a system, such as Lyapunov exponents and dimension [15].…”

Section: Nonlinear Analysis Of Speech Signalsmentioning

confidence: 99%

Sub-banded reconstructed phase spaces for speech recognition

Indrebo

Povinelli

Johnson

2006

Speech Communication

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ii Preface A novel method for classification of speech phonemes, based on the combination of dynamical systems theory and filter banks, is introduced. The benefit of this approach is seen in its ability to model nonlinear characteristics of speech, something that traditional methods cannot do. The modeling tool that provides this capability is the reconstructed phase space. This space carries all the dynamical information present in the signal's underlying system. The reconstructed phase spaces used for modeling and classification of the phonemes are built using frequency sub-banded signals that are generated using a set of band-pass filters. This approach is motivated by empirical evidence that suggests humans process and recognize speech in sub-bands. Modeling and classification is performed on the sub-banded reconstructed phase spaces using Gaussian Mixture Models, and the results of the classifications for each sub-band are combined to form an overall classification. Several methods for the combination of the sub-band classifications are examined, and it is found that an un-weighted linear combination produces classification accuracies that are significantly higher than those of a classification system using reconstructed phase spaces of unfiltered signals. Results also demonstrate that the proposed phoneme classification system is competitive with state-of-the-art approaches.iii

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Time series classification using Gaussian mixture models of reconstructed phase spaces

Cited by 143 publications

References 18 publications

An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation

An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation

Towards the prediction of transient ST changes

Sub-banded reconstructed phase spaces for speech recognition

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