Clustering and Anomaly Detection with Kernels

This chapter treats the relevant topic of clustering and anomaly detection with kernels. The field is in the core of machine learning, and has many practical implications. The chapter discusses the kernel‐based approaches systematically. They are clustering, density estimation (sometimes...

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Vydáno v:Digital Signal Processing with Kernel Methods s. 503 - 542
Hlavní autoři: Rojo-Álvarez, José Luis, Martínez-Ramón, Manel, Muñoz-Mar&iacute, Jordi, Camps-Valls, Gustau
Médium: Kapitola
Jazyk:angličtina
Vydáno: Chichester, UK Wiley 2018
John Wiley & Sons, Ltd
Vydání:1
Edice:Wiley - IEEE
Témata:
anomaly detection
clustering
digital signal processing
kernel method
statistical hypothesis testing
support vector domain description
support vectors
Hilbert space
Kernel
Clustering algorithms
Signal processing algorithms
Estimation
Anomaly detection
ISBN:9781118611791, 1118611799
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Shrnutí:This chapter treats the relevant topic of clustering and anomaly detection with kernels. The field is in the core of machine learning, and has many practical implications. The chapter discusses the kernel‐based approaches systematically. They are clustering, density estimation (sometimes referred as to domain description), matched subspace detectors, anomaly change detection, and statistical hypothesis testing. Kernel clustering is based on reformulating existing clustering methods with kernels. Such reformulation, nevertheless, can take two different pathways: either “kernelize” a standard clustering algorithm that relies solely on dot products between samples or that relies on distances between samples. As an alternative to the previous approaches for clustering with kernels, the description of the domain can be done via support vectors. This idea leads to several algorithms, such as the one‐class support vector machine (OC‐SVM) and the related support vector domain description (SVDD).
ISBN:9781118611791
1118611799
DOI:10.1002/9781118705810.ch11