منابع مشابه
Making Archetypal Analysis Practical
Archetypal analysis represents the members of a set of multivariate data as a convex combination of extremal points of the data. It allows for dimensionality reduction and clustering and is particularly useful whenever the data are superpositions of basic entities. However, since its computation costs grow quadratically with the number of data points, the original algorithm hardly applies to mo...
متن کاملWeighted and robust archetypal analysis
Archetypal analysis represents observations in a multivariate data set as convex combinations of a few extremal points lying on the boundary of the convex hull. Data points which vary from the majority have great influence on the solution; in fact one outlier can break down the archetype solution. This paper adapts the original algorithm to be a robust M-estimator and presents an iteratively re...
متن کاملArchetypal Analysis of Interval Data
In this paper we present a mathematical model for archetypal analysis of data represented by means of intervals of real numbers. We extend the model for single-valued data proposed in the pioneering work of Cutler and Breiman on this topic. The core problem is a non-convex optimization one, which we solve by means of a sequential quadratic programming method. We show numerical experiments perfo...
متن کاملA Probabilistic Weighted Archetypal Analysis Method with Earth Mover's Distance for Endmember Extraction from Hyperspectral Imagery
A Probabilistic Weighted Archetypal Analysis method with Earth Mover’s Distance (PWAA-EMD) is proposed to extract endmembers from hyperspectral imagery (HSI). The PWAA-EMD first utilizes the EMD dissimilarity matrix to weight the coefficient matrix in the regular Archetypal Analysis (AA). The EMD metric considers manifold structures of spectral signatures in the HSI data and could better quanti...
متن کاملArchetypal Analysis of Cellular Flame Data
The application of archetypal analysis to high-dimensional data arising from video-taped images is presented. A hybrid principal components/archetypes technique has been developed to overcome the difficulties of applying archetypes to data sets with points living in a space of dimension higher than about 500. The advantages of the method lie in the creation of patterns typical of the set as a w...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2015
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-015-5498-8