OPUS


Hankel matrices for use in Learning Vector Quantization

  • Classification of time series has received an important amount of interest over the past years due to many real-life applications, such as environmental modeling, speech recognition, and computer vision. In my thesis, I focus on classification of time series by LVQ classifiers. To learn a classifiers, we need a training set. In our case, every data point in the training set contains a sequence (an ordered set) of feature vectors. Thus, the first task is to construct a new feature vector (or matrix) for each sequence. Inspired by [2], I use Hankel matrices to construct the new feature vectors. This choice comes from a basic assumption that each time series is generated by a single or a set of unknown Linear Time Invariant (LTI) systems. After generating new feature vectors by Hankel matrices, I use two approaches to learn a classifier: Generalized Learning Vector Quntization (GLVQ) and Median variant of Generalized Learning Vector Quantization (mGLVQ).

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Metadaten
Author:Mohammad Mohammadi
URN:urn:nbn:de:bsz:mit1-opus4-79493
Document Type:Master's Thesis
Language:English
Year of Completion:2016
Granting Institution:Hochschule Mittweida
Release Date:2017/10/09
GND Keyword:Zeitreihe , Vektor , Hankel-Matrix
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
Access Rights:Frei zugänglich
Licence (German):License LogoEs gilt das UrhG

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