Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning

Nebel, David; Kaden, Marika; Villmann, Andrea; Villmann, Thomas

doi:10.1016/j.neucom.2016.12.091

Cited by 22 publications

(13 citation statements)

References 44 publications

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“…If the number of samples N ≤ 1000, then the rank parameter k 30, otherwise k 100. 3 The shift parameter λ is calculated on the low-rank approximated matrix, using a von Mises or power iteration [59] to determine the respective largest negative eigenvalue of the matrix. As shift parameter, we use the absolute value of λ for further steps.…”

Section: Advanced Shift Correctionmentioning

confidence: 99%

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Data-Driven Supervised Learning for Life Science Data

Münch

Raab

Biehl

et al. 2020

Front. Appl. Math. Stat.

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Life science data are often encoded in a non-standard way by means of alphanumeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms.

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Section: Advanced Shift Correctionmentioning

confidence: 99%

“…In the following, we expect that these proximities are at least symmetric, but do not necessarily obey metric properties. See e.g., [3] for an extended discussion.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Supervised Learning for Life Science Data

Münch

Raab

Biehl

et al. 2020

Front. Appl. Math. Stat.

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“…where d is a given dissimilarity measure [11]. We denote w s(x) as the winner prototype of the competition.…”

Section: Learning Vector Quantizers For Prototype Based Classification a Standard Learning Vector Quantizationmentioning

confidence: 99%

“…A disadvantage of MLP or deep architectures is that the trained networks are essentially black-box systems, i. e. the functionality of each subnet/ layer is difficult to interpret. This is in contrast to prototype based networks like learning vector quantization for classification (LVQ, [8], [9]), which are intuitively understandable due to their reference principle based on dissimilarity comparisons between data and representative prototypes [10], [11]. Further, in recent years, many improvements of the basic LVQ schemes were proposed which have led to a strong mathematical foundation based on cost functions (Generalized LVQ -GLVQ [43]) as well as to sophisticated variants covering many advanced learning problems like automatic dissimilarity adaptation and feature relevance learning, learning from imbalanced data or optimization of statistical measure instead of simple accuracy learning [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning

Villmann

Biehl

Villmann

et al. 2017

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

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The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust behavior nowadays powerful alternatives have increasing attraction. Particularly, deep architectures of multilayer networks achieve frequently very high accuracies and are, thanks to modern graphic processor units use for calculation, trainable in acceptable time.In this conceptual paper we show, how we can combine both network architectures to benefit from their advantages. For this purpose, we consider learning vector quantizers in terms of feedforward network architectures and explain how it can be combined effectively with multilayer or single-layer feedforward network architectures. This approach includes deep and flat architectures as well as the popular extreme learning machines. For the resulting networks, the multi-/ single-layer networks act as adaptive filters like in signal processing while the interpretability of the prototype-based learning vector quantizers is kept for the resulting filtered feature space. In this way a powerful combination of two successful architectures is obtained.

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“…The most prominent example is that kernels as inner products in a Hilbert space do not necessarily be similarity measures. For a respective discussion we refer to [40,59].…”

Section: Beyond the Euclidean World -Glvq With Non-standard Dissimilamentioning

confidence: 99%

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Villmann

Bohnsack

Kaden

2016

Journal of Artificial Intelligence and Soft Computing Research

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Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classification of vector data, intuitively introduced by Kohonen. The prototype adaptation scheme relies on its attraction and repulsion during the learning providing an easy geometric interpretability of the learning as well as of the classification decision scheme. Although deep learning architectures and support vector classifiers frequently achieve comparable or even better results, LVQ models are smart alternatives with low complexity and computational costs making them attractive for many industrial applications like intelligent sensor systems or advanced driver assistance systems.Nowadays, the mathematical theory developed for LVQ delivers sufficient justification of the algorithm making it an appealing alternative to other approaches like support vector machines and deep learning techniques.This review article reports current developments and extensions of LVQ starting from the generalized LVQ (GLVQ), which is known as the most powerful cost function based realization of the original LVQ. The cost function minimized in GLVQ is an soft-approximation of the standard classification error allowing gradient descent learning techniques. The GLVQ variants considered in this contribution, cover many aspects like bordersensitive learning, application of non-Euclidean metrics like kernel distances or divergences, relevance learning as well as optimization of advanced statistical classification quality measures beyond the accuracy including sensitivity and specificity or area under the ROC-curve.According to these topics, the paper highlights the basic motivation for these variants and extensions together with the mathematical prerequisites and treatments for integration into the standard GLVQ scheme and compares them to other machine learning approaches. For detailed description and mathematical theory behind all, the reader is referred to the respective original articles.Thus, the intention of the paper is to provide a comprehensive overview of the stateof-the-art serving as a starting point to search for an appropriate LVQ variant in case of a given specific classification problem as well as a reference to recently developed variants and improvements of the basic GLVQ scheme.

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Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning

Cited by 22 publications

References 44 publications

Data-Driven Supervised Learning for Life Science Data

Data-Driven Supervised Learning for Life Science Data

Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

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