Learning vector quantization and relevances in complex coefficient space

AbstractIn this contribution, we consider the classification of time series and similar functional data which can be represented in complex Fourier and wavelet coefficient space. We apply versions of learning vector quantization (LVQ) which are suitable for complex-valued data, based on the so-called Wirtinger calculus. It allows for the formulation of gradient-based update rules in the framework of cost-function-based generalized matrix relevance LVQ (GMLVQ). Alternatively, we consider the concatenation of real and imaginary parts of Fourier coefficients in a real-valued feature vector and the classification of time-domain representations by means of conventional GMLVQ. In addition, we consider the application of the method in combination with wavelet-space features to heartbeat classification.

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Classification of MRI Brain Tumor and Mammogram Images using Adaboost and Learning Vector Quantization Neural Network

2020 International Conference on Convergence to Digital World - Quo Vadis (ICCDW) ◽

10.1109/iccdw45521.2020.9318645 ◽

2020 ◽

Author(s):

Ravindra Sugdeo Sonavane ◽

Adhyayan Sugdeo Sonavane

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Vector Quantization ◽

Learning Vector Quantization ◽

Mri Brain ◽

Mammogram Images

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Image Classification of Ribbed Smoked Sheet using Learning Vector Quantization

Journal of Physics Conference Series ◽

10.1088/1742-6596/801/1/012050 ◽

2017 ◽

Vol 801 ◽

pp. 012050 ◽

Cited By ~ 3

Author(s):

R F Rahmat ◽

A F Pulungan ◽

S Faza ◽

R Budiarto

Keyword(s):

Image Classification ◽

Vector Quantization ◽

Learning Vector Quantization

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Automatic Classification of Woven Fabric Structure by Using Learning Vector Quantization

Procedia Engineering ◽

10.1016/j.proeng.2011.08.930 ◽

2011 ◽

Vol 15 ◽

pp. 5005-5009 ◽

Cited By ~ 5

Author(s):

Junfeng Jing ◽

Jing Wang ◽

Pengfei Li ◽

Yang Li

Keyword(s):

Vector Quantization ◽

Automatic Classification ◽

Learning Vector Quantization ◽

Woven Fabric ◽

Fabric Structure

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Classification of Human Body Smell by Learning Vector Quantization

Distributed Computing and Artificial Intelligence, 15th International Conference - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-94649-8_11 ◽

2018 ◽

pp. 86-93

Author(s):

Sigeru Omatu

Keyword(s):

Vector Quantization ◽

Human Body ◽

Learning Vector Quantization

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Classification of Landsat 8 Imagery Using Kohonen’s Self Organizing Maps and Learning Vector Quantization

Lecture Notes in Electrical Engineering - Advances in Communication, Signal Processing, VLSI, and Embedded Systems ◽

10.1007/978-981-15-0626-0_35 ◽

2019 ◽

pp. 445-462

Author(s):

B. R. Shivakumar ◽

S. V. Rajashekararadhya

Keyword(s):

Vector Quantization ◽

Learning Vector Quantization ◽

Landsat 8 ◽

Self Organizing Maps ◽

Self Organizing

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Classification of Textual Documents Using Learning Vector Quantization

Information Technology Journal ◽

10.3923/itj.2007.154.159 ◽

2006 ◽

Vol 6 (1) ◽

pp. 154-159 ◽

Cited By ~ 21

Author(s):

Muhammad Fahad Umer ◽

M. Sikander Hayat Khiyal

Keyword(s):

Vector Quantization ◽

Learning Vector Quantization

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Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16183294 ◽

2019 ◽

Vol 16 (18) ◽

pp. 3294 ◽

Cited By ~ 1

Author(s):

Piotr Boniecki ◽

Małgorzata Idzior-Haufa ◽

Agnieszka Pilarska ◽

Krzysztof Pilarski ◽

Alicja Kolasa-Wiecek

Keyword(s):

Vector Quantization ◽

Digital Images ◽

Neural Model ◽

Learning Vector Quantization ◽

Neural Models ◽

Topological Map ◽

Compost Maturity ◽

Feature Map ◽

Graphic Information

Self-Organising Feature Map (SOFM) neural models and the Learning Vector Quantization (LVQ) algorithm were used to produce a classifier identifying the quality classes of compost, according to the degree of its maturation within a period of time recorded in digital images. Digital images of compost at different stages of maturation were taken in a laboratory. They were used to generate an SOFM neural topological map with centres of concentration of the classified cases. The radial neurons on the map were adequately labelled to represent five suggested quality classes describing the degree of maturation of the composted organic matter. This enabled the creation of a neural separator classifying the degree of compost maturation based on easily accessible graphic information encoded in the digital images. The research resulted in the development of original software for quick and easy assessment of compost maturity. The generated SOFM neural model was the kernel of the constructed IT system.

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Spectral regularization in generalized matrix learning vector quantization

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) ◽

10.1109/wsom.2017.8020029 ◽

2017 ◽

Author(s):

David Nova ◽

Pablo A. Estevez

Keyword(s):

Vector Quantization ◽

Learning Vector Quantization ◽

Spectral Regularization ◽

Generalized Matrix

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Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization

Technology and Health Care ◽

10.3233/thc-1998-6108 ◽

1998 ◽

Vol 6 (1) ◽

pp. 65-74 ◽

Cited By ~ 32

Author(s):

L. Pesu ◽

P. Helistö ◽

E. Ademovič ◽

J.-C. Pesquet ◽

A. Saarinen ◽

...

Keyword(s):

Vector Quantization ◽

Wavelet Packet ◽

Learning Vector Quantization ◽

Respiratory Sounds ◽

Wavelet Packet Decomposition

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Neural Classifiers for Automated Visual Inspection

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1243/pime_proc_1994_208_166_02 ◽

1994 ◽

Vol 208 (2) ◽

pp. 83-89 ◽

Cited By ~ 14

Author(s):

D T Pham ◽

E J Bayro-Corrochano

Keyword(s):

Vector Quantization ◽

Visual Inspection ◽

Vision System ◽

Back Propagation ◽

Learning Vector Quantization ◽

Multi Layer Perceptron ◽

Automated Visual Inspection ◽

Valve Stem

This paper discusses the application of a back-propagation multi-layer perceptron and a learning vector quantization network to the classification of defects in valve stem seals for car engines. Both networks were trained with vectors containing descriptive attributes of known flaws. These attribute vectors (‘signatures’) were extracted from images of the seals captured by an industrial vision system. The paper describes the hardware and techniques used and the results obtained.

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