Archetypal analysis for machine learning

207 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Prototypal Analysis and Prototypal Regression (2017, background): “ data points are required (Scho¨lkopf and Smola, 2002; Shawe-Taylor and Cristianini, 2004; Hofmann et al., 2008). This is the case of archetypal analysis, which can therefore be extended via kernels (Mørup and Hansen, 2012). Examples of application can be found in time series clustering (Bauckhage a”
Multiple endmembers based unmixing using Archetypal Analysis (2015, methodology): “The iterative process of AA shows in [4] that the calculations in the process are determined by the inner product of samples, namely considered as kernel matrix of T  K X X .”
From traits to life‐history strategies: Deconstructing fish community composition across European seas (2017, unknown): “The core of the 161 AA approach is the identification of points (archetypes) forming the corners of the convex hull volume 162 encompassing the trait space (Mørup & Hansen 2012): points are then represented by the proportions 163 based on the proximity of the point to each archetype.”
Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series (2014, background): “Kernel archetypal analysis was hinted at in [3] and demonstrated in terms of a toy example in [7].”