Prototype‐based models in machine learning

Biehl, Michael; Hammer, Barbara; Villmann, Thomas

doi:10.1002/wcs.1378

Cited by 108 publications

(107 citation statements)

References 72 publications

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“…Then the primary shapes -the prototypes or archetypes -in the data can be captured by the centers of the detected clusters (see e.g. [19]). We could perform the clustering directly in the high-dimensional feature space before encoding, but this is often computationally unfeasible.…”

Section: B Prototype Extractionmentioning

confidence: 99%

A Flexible Framework for Anomaly Detection via Dimensionality Reduction

Sadr

Bassett

Kunz

2019

2019 6th International Conference on Soft Computing &Amp; Machine Intelligence (ISCMI)

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Anomaly detection is challenging, especially for large datasets in high dimensions. Here we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. We release DRAMA, a general python package that implements the general framework with a wide range of built-in options. We test DRAMA on a wide variety of simulated and real datasets, in up to 3000 dimensions, and find it robust and highly competitive with commonly-used anomaly detection algorithms, especially in high dimensions.The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning and highly unbalanced datasets.

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Section: B Prototype Extractionmentioning

confidence: 99%

A Flexible Framework for Anomaly Detection via Dimensionality Reduction

Sadr

Bassett

Kunz

2019

2019 6th International Conference on Soft Computing &Amp; Machine Intelligence (ISCMI)

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“…Several ARD methods are available within the family of LVQ (learning vector quantization), all of which use an adaptive metric in the similarity estimation [3]. Similarity is a crucial concept of quantitative discriminant analysis.…”

Section: Automatic Relevance Determinationmentioning

confidence: 99%

EOG feature relevance determination for microsleep detection

Gölz

Wollner

Sommer

et al. 2017

Current Directions in Biomedical Engineering

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Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learning vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 -4.9 % and 1.9 -10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 -0.006 % and 0.002 -0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6  1.3 % and 92.3  0.2 % for GRLVQ and SVM, respectively. GRLVQ permits objective feature reduction by inclusion of all processing stages, but is not as accurate as SVM.

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“…Here, however, the focus is on the study of localized, but explicitly time-dependent densities of high-dimensional inputs in a stream of training examples. More specifically, we consider Learning Vector Quantization (LVQ) as a prototype-based framework for classification [9,10,11]. LVQ systems are most frequently trained in an online setting by presenting a sequence of single examples for iterative adaptation [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, we consider Learning Vector Quantization (LVQ) as a prototype-based framework for classification [9,10,11]. LVQ systems are most frequently trained in an online setting by presenting a sequence of single examples for iterative adaptation [10,11,12]. Hence, LVQ should constitute a natural tool for incremental learning in non-stationary environments [4].…”

Section: Introductionmentioning

confidence: 99%

Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework

Biehl

Abadi

Göpfert

et al. 2019

Advances in Intelligent Systems and Computing

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We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments. Specifically, we study Learning Vector Quantization (LVQ) systems trained from a stream of high-dimensional, clustered data. We consider standard winnertakes-all updates known as LVQ1. Statistical properties of the input data change on the time scale defined by the training process. We apply analytical methods borrowed from statistical physics which have been used earlier for the exact description of learning in stationary environments. The suggested framework facilitates the computation of learning curves in the presence of virtual and real concept drift. Here we focus on timedependent class bias in the training data. First results demonstrate that, while basic LVQ algorithms are suitable for the training in non-stationary environments, weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes.

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Prototype‐based models in machine learning

Cited by 108 publications

References 72 publications

A Flexible Framework for Anomaly Detection via Dimensionality Reduction

A Flexible Framework for Anomaly Detection via Dimensionality Reduction

EOG feature relevance determination for microsleep detection

Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework

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