scholarly journals Adaptive Relevance Matrices in Learning Vector Quantization

2009 ◽  
Vol 21 (12) ◽  
pp. 3532-3561 ◽  
Author(s):  
Petra Schneider ◽  
Michael Biehl ◽  
Barbara Hammer
Keyword(s):  
Vector Quantization ◽  
Classification Scheme ◽  
Distance Measure ◽  
Learning Vector Quantization ◽  
Data Set ◽  
Full Matrix ◽  
C Elegans ◽  
Generalization Bounds ◽  
Multiclass Problem ◽  
Decision Boundaries

We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and general metric adaptation takes place during training. In comparison to the weighted Euclidean metric used in RLVQ and its variations, a full matrix is more powerful to represent the internal structure of the data appropriately. Large margin generalization bounds can be transferred to this case, leading to bounds that are independent of the input dimensionality. This also holds for local metrics attached to each prototype, which corresponds to piecewise quadratic decision boundaries. The algorithm is tested in comparison to alternative learning vector quantization schemes using an artificial data set, a benchmark multiclass problem from the UCI repository, and a problem from bioinformatics, the recognition of splice sites for C. elegans.

scholarly journals Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Trevor J. Bihl ◽  
Todd J. Paciencia ◽  
Kenneth W. Bauer ◽  
Michael A. Temple
Keyword(s):  
Vector Quantization ◽  
Distance Measure ◽  
Critical Infrastructure ◽  
Low Cost ◽  
Learning Vector Quantization ◽  
Distance Measures ◽  
Rf Signals ◽  
Communication Devices ◽  
Rf Fingerprinting ◽  
Relevance Learning

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and PVs are iteratively moved to accurately represent the data. GRLVQI extends LVQ with a sigmoidal cost function, relevance learning, and PV update logic improvements. However, both LVQ and GRLVQI are limited due to a reliance on squared Euclidean distance measures and a seemingly complex algorithm structure if changes are made to the underlying distance measure. Herein, the authors (1) develop GRLVQI-D (distance), an extension of GRLVQI to consider alternative distance measures and (2) present the Cosine GRLVQI classifier using this framework. To evaluate this framework, the authors consider experimentally collected Z-wave RF signals and develop RF fingerprints to identify devices. Z-wave devices are low-cost, low-power communication technologies seen increasingly in critical infrastructure. Both classification and verification, claimed identity, and performance comparisons are made with the new Cosine GRLVQI algorithm. The results show more robust performance when using the Cosine GRLVQI algorithm when compared with four algorithms in the literature. Additionally, the methodology used to create Cosine GRLVQI is generalizable to alternative measures.

scholarly journals Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

2012 ◽  
Vol 60 (2) ◽  
pp. 69-72 ◽  
Author(s):  
Jiří Fejfar ◽  
Jiří Šťastný ◽  
Miroslav Cepl
Keyword(s):  
Time Series ◽  
Vector Quantization ◽  
Multilayer Perceptron ◽  
Confusion Matrix ◽  
A Priori ◽  
Learning Vector Quantization ◽  
Self Organizing Map ◽  
Data Set ◽  
Nearest Neighbours ◽  
Musical Excerpts

We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL), an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ) algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM).After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to), we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.

Soft Learning Vector Quantization

2003 ◽  
Vol 15 (7) ◽  
pp. 1589-1604 ◽  
Author(s):  
Sambu Seo ◽  
Klaus Obermayer
Keyword(s):  
Vector Quantization ◽  
Gradient Descent ◽  
Distance Measure ◽  
Learning Rule ◽  
Gaussian Mixture ◽  
Classification Performance ◽  
Learning Vector Quantization ◽  
Additional Benefit ◽  
New Approach ◽  
New Methods

Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of “soft” LVQ algorithms. Benchmark results show that the new methods lead to better classification performance than LVQ 2.1. An additional benefit of the new method is that model assumptions are made explicit, so that the method can be adapted more easily to different kinds of problems.

scholarly journals PENGENALAN POLA SIDIK JARI DENGAN METODE LOCAL BINARY PATTERN DAN LEARNING VECTOR QUANTIZATION

2019 ◽  
Vol 7 (2) ◽  
pp. 148-156
Author(s):  
Adriana Fanggidae ◽  
Dony M Sihotang ◽  
Adnan Putra Rihi Pati
Keyword(s):  
Vector Quantization ◽  
Local Binary Pattern ◽  
Learning Vector Quantization ◽  
Data Set ◽  
Otsu Thresholding

Sidik jari merupakan strukur genetika dalam bentuk pola yang sangat detail dan tanda yang melekat pada diri manusia. Banyak sistem biometrika yang menggunakan sidik jari sebagai data masukan, karena sifat dari sidik jari setiap individu berbeda meskipun kembar identik dan tidak berubah kecuali mendapat kecelakaan. Metode yang digunakan dalam penelitian ini yaitu segmentasi dengan algoritma Otsu thresholding, ekstraksi ciri dengan algoritma Local Binary Pattern (LBP), dan pembelajaran dengan algoritma Learning Vector Quantization (LVQ). Data yang digunakan adalah citra sidik jari jempol berukuran 200 x 300 piksel, berjenis keabuan dan berformat *.jpg. Citra sidik jari terdiri dari 25 orang, masing-masing orang memiliki 6 data latih dan 2 data uji. Pengujian data latih dan data uji dilakukan kepada empat sistem yaitu sistem dengan jumlah ciri LBP = 8, 64, 128 dan 256 dan menggunakan masing-masing 2 buah data set dimana data set 1 berjumlah 15 orang dan data set 2 berjumlah 25 orang. Hasil pengujian keempat sistem menunjukkan bahwa sistem dengan jumlah ciri LBP = 128 merupakan sistem yang terbaik dengan kombinasi akurasi sistem yang tinggi dan juga waktu pembelajaran yang cepat.

Penerapan Learning Vector Quantization Dalam Memprediksi Jumlah Rumah Tangga Miskin

2019 ◽  
Vol 5 (2) ◽  
pp. 118-127
Author(s):  
Harliana Harliana ◽  
Sodik Kirono
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Input Output ◽  
Data Set

Kemiskinan merupakan salah satu permasalahan penting yang masih terus dilakukan pengkajiannya oleh pemerintah daerah termasuk pemerintah Kabupaten Cirebon, berbagai upaya pemberian bantuan telah dilakukan, namun sayangnya masih ada beberapa rumah tangga miskin yang belum dapat keluar dari kemiskinan tersebut. Penelitian ini bertujuan untuk memprediksi rumah tangga miskin yang telah mendapatkan bantuan, apakah dapat keluar dari kemiskinannya ataukah tetap pada kelompok desil 1 (rumah tangga sangat miskin), desil 2 (rumah tangga miskin), desil 3 (rumah tangga hampir miskin) melalui algoritma LVQ. Algoritma LVQ merupakan salah satu algoritma klasifikasi yang mampu mengenali dan meniru input output yang telah ditentukan. Penelitian ini menggunakan 70 data set, 10 neuron inputan, 3 neuron keluaran, 100 MaxEpoh dan 0,05 learning rate (a) dalam melakukan prediksi. Dari 70 data set yang digunakan selanjutnya akan dipecah menjadi data training dan data testing. Berdasarkan 5 hasil pengujian yang telah dilakukan, didapatkan bahwa tingkat akurasi dan error rate akan berbanding lurus terhadap jumlah data training dan data testing yang ditentukan.

How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds
Keyword(s):  
Test Data ◽  
Survival Data ◽  
Prediction Error ◽  
Classification Scheme ◽  
Neural Nets ◽  
Brier Score ◽  
Data Set ◽  
Independent Test ◽  
Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

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