Archetypal analysis of galaxy spectra

By B. Chan, D. Mitchell, Lawrence Cram. 2003

66 citations recorded by the source corpus.

ABSTRACT Archetypal analysis represents each individual member of a set of data vectors as a mixture (aconstrained linear combination)of the pure types or archetypes of the data set. The archetypesare themselves required to be mixtures of the data vectors. Archetypal analysis may be partic-ularlyuseful in analysingdata sets comprisinggalaxyspectra, since each spectrumis, presum-ably, a superposition of the emission from the various stellar populations, nebular emissionsand nuclear activity making up that galaxy, and each of these emission sources corresponds toa potential archetype of the entire data set. We demonstrate archetypal analysis using sets ofcomposite synthetic galaxy spectra, showing that the method promises to be an effective andefficient way to classify spectra. We show that archetypal an alysis is robust in the presence ofvarious types of noise.Key words: methods: data analysis – methods: statistical – galaxies: e volution – galaxies:fundamental parameters – galaxies: stellar content

Subfields: Analytical Chemistry, Astronomy and Astrophysics

How researchers see this paper

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

In Analytical Chemistry

1. Christian Bauckhage
2. Christian Thurau
3. Manuel J. A. Eugster
4. Morten Mørup
5. Esteban G. Tabak
6. Chenyue Wu

In Astronomy and Astrophysics

1. Manuel J. A. Eugster
2. Esteban G. Tabak
3. Chenyue Wu

Citations in the corpus

• Prototypal Analysis and Prototypal Regression (2017, methodology): “Archetypal analysis has been applied in physics (Stone and Cutler, 1996; Stone, 2002; Chan et al., 2003), biology (Huggins et al., 2007; Römer et al., 2012; Thøgersen et al., 2013), psychology (Thurau and Drachen, 2011; Drachen et al., 2012, 2016; Sifa and Bauckhage, 2013), marketing (Li et al.,…”
• Archetypal analysis for machine learning (2010, background): “Recently, archetypal analysis has found use in benchmarking and market research identifying typically extreme practices, rather than just good practices [26] as well as in the analysis of astronomy spectra [5] as an approach for the end-member extraction problem [25].”
• Performance Profiles based on Archetypal Athletes (2012, methodology): “This statistical method was first introduced by Cutler and Breiman (1994) and has found applications in different areas, e.g., in economics (Li et al., 2003; Porzio et al., 2008), astrophysics (Chan et al., 2003) and pattern recognition (Bauckhage and Thurau, 2009).”
• Making Archetypal Analysis Practical (2009, unknown): “So far, however, AA has found application in physics and biology [6,7,8] but did not prevail as a commodity tool for pattern analysis or classification.”
• Archetypal Images in Large Photo Collections (2009, background): “Although archetypal analysis offers interesting perspectives for soft clustering, probabilistic ranking, or classification using latent class models and has been applied in physics and biology [9]–[11], it did not yet prevail as a commodity tool for data analysis.”
• On the use of archetypes as benchmarks (2008, unknown): “Archetypal analysis was first introduced by Cutler and Breiman [1] and has found applications in spatio-temporal dynamics and cellular flames [2, 3], in astronomy [4], and in market research [5–7].”