Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series

Bauckhage, Christian; Manshaei, Kasra

doi:10.1109/icpr.2014.274

Cited by 10 publications

(8 citation statements)

References 26 publications

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“…section of this paper and by previous studies (Mørup and Hansen 2012;Bauckhage 2014). Other pattern recognition types discussed here are non-negative matrix factorization (Cichocki et al 2009;Gillis 2020;Mairal 2014Mairal , 2017, clustering, and optimisation on manifold (Boumal et al 2014;Hannachi and Trendafilov 2017;Hannachi 2021;Trendafilov and Gallo 2021).…”

Section: Introductionmentioning

confidence: 84%

Archetypal Analysis of Geophysical Data Illustrated by Sea Surface Temperature

Black

Monselesan

Risbey

et al. 2022

Artificial Intelligence for the Earth Systems

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The ability to find and recognize patterns in high-dimensional geophysical data is fundamental to climate science and critical for meaningful interpretation of weather and climate processes. Archetypal analysis (AA) is one technique that has recently gained traction in the geophysical science community for its ability to find patterns based on extreme conditions. While traditional empirical orthogonal function (EOF) analysis can reveal patterns based on data covariance, AA seeks patterns from the points located at the edges of the data distribution. The utility of any objective pattern method depends on the properties of the data to which it is applied and the choices made in implementing the method. Given the relative novelty of the application of AA in geophysics it is important to develop experience in applying the method. We provide an assessment of the method, implementation, sensitivity, and interpretation of AA with respect to geophysical data. As an example for demonstration, we apply AA to a 39-year sea surface temperature (SST) reanalysis data set. We show that the decisions made to implement AA can significantly affect the interpretation of results, but also, in the case of SST, that the analysis is exceptionally robust under both spatial and temporal coarse-graining.

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Section: Introductionmentioning

confidence: 84%

Archetypal Analysis of Geophysical Data Illustrated by Sea Surface Temperature

Black

Monselesan

Risbey

et al. 2022

Artificial Intelligence for the Earth Systems

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“…Model selection is the topic of Prabhakaran et al (2012), where the authors are concerned with the optimal number of archetypes needed to characterize a given data set. An extension of the original Archetypal Analysis model to non-linear kernel Archetypal Analysis is proposed by Bauckhage and Manshaei (2014); Mørup and Hansen (2012). In Kaufmann et al (2015), the authors use a copula based approach to make AA independent of strictly monotone transformations of the input data.…”

Section: Related Workmentioning

confidence: 99%

Learning Extremal Representations with Deep Archetypal Analysis

et al. 2020

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Archetypes represent extreme manifestations of a population with respect to specific characteristic traits or features. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. As mixing of archetypes is performed directly on the input data, linear Archetypal Analysis requires additivity of the input, which is a strong assumption unlikely to hold e.g. in case of image data. To address this problem, we propose learning an appropriate latent feature space while simultaneously identifying suitable archetypes. We thus introduce a generative formulation of the linear archetype model, parameterized by neural networks. By introducing the distance-dependent archetype loss, the linear archetype model can be integrated into the latent space of a deep variational information bottleneck and an optimal representation, together with the archetypes, can be learned end-to-end. Moreover, the information bottleneck framework allows for a natural incorporation of arbitrarily complex side information during training. As a consequence, learned archetypes become easily interpretable as they derive their meaning directly from the included side information. Applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information. A second application illustrates the exploration of the chemical space of small organic molecules. By using different kinds of side information we demonstrate how identified archetypes, along with their interpretation, largely depend on the side information provided.

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“…Archetypal Analysis proposes a matrix factorization algorithm based on the data points lying on the convex hull of the dataset which are the closest concepts to peripheral vertices of a graph. [17] and [18] propose an efficient version of Archetypal Analysis and a kernelized version of this algorithm respectively.…”

Section: A Previous Workmentioning

confidence: 99%

SGPD Volume Maximization for Community Detection

Manshaei,

Bauckhage

2015

Preprint

Self Cite

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In this note we briefly study the feasibility of community detection in complex networks using peripheral vertices. Our method suggests a novel direction in axiomizing the problem of clustering in graphs and complex networks by looking at the topological role each vertex plays in the community structure, regardless of the attributes. The promising strength of pseudoperipheral vertices as a lever for analysis of complex networks is also demonstrated on real-world data.

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Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series

Cited by 10 publications

References 26 publications

Archetypal Analysis of Geophysical Data Illustrated by Sea Surface Temperature

Archetypal Analysis of Geophysical Data Illustrated by Sea Surface Temperature

Learning Extremal Representations with Deep Archetypal Analysis

SGPD Volume Maximization for Community Detection

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