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Suleman, A. (2017). Validation of archetypal analysis. In 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017. Naples: IEEE.
Export Reference (IEEE) 
A. K. Suleman,  "Validation of archetypal analysis", in 2017 IEEE Int. Conf. on Fuzzy Systems, FUZZ 2017, Naples, IEEE, 2017
Export BibTeX 
@inproceedings{suleman2017_1766409564699,
	author = "Suleman, A.",
	title = "Validation of archetypal analysis",
	booktitle = "2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017",
	year = "2017",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/FUZZ-IEEE.2017.8015385",
	publisher = "IEEE",
	address = "Naples",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/8015385/"
}
Export RIS 
TY  - CPAPER
TI  - Validation of archetypal analysis
T2  - 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
AU  - Suleman, A.
PY  - 2017
SN  - 1558-4739
DO  - 10.1109/FUZZ-IEEE.2017.8015385
CY  - Naples
UR  - https://ieeexplore.ieee.org/document/8015385/
AB  - We use an information-theoretic criterion to assess the goodness-of-fit of the output of archetypal analysis (AA), also intended as a fuzzy clustering tool. It is an adaptation of an existing AIC-like measure to the specifics of AA. We test its effectiveness using artificial data and some data sets arising from real life problems. In most cases, the results achieved are similar to those provided by an external similarity index. The average reconstruction accuracy is about 93%.
ER  -