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Suleman, A. (2017). On ill-conceived initialization in archetypal analysis. Advances in Data Analysis and Classification. 11 (4), 785-808
A. K. Suleman, "On ill-conceived initialization in archetypal analysis", in Advances in Data Analysis and Classification, vol. 11, no. 4, pp. 785-808, 2017
@article{suleman2017_1711705945721, author = "Suleman, A.", title = "On ill-conceived initialization in archetypal analysis", journal = "Advances in Data Analysis and Classification", year = "2017", volume = "11", number = "4", doi = "10.1007/s11634-017-0303-0", pages = "785-808", url = "https://link.springer.com/article/10.1007/s11634-017-0303-0" }
TY - JOUR TI - On ill-conceived initialization in archetypal analysis T2 - Advances in Data Analysis and Classification VL - 11 IS - 4 AU - Suleman, A. PY - 2017 SP - 785-808 SN - 1862-5347 DO - 10.1007/s11634-017-0303-0 UR - https://link.springer.com/article/10.1007/s11634-017-0303-0 AB - We show that an improper initialization of the matrix of prototypes, V, can be misleading, and potentially gives rise to a degenerate fuzzy partition when performing fuzzy clustering by means of an archetypal analysis. Subsequently, we propose an algorithm to correct the initial guess for V, which is grounded in two theoretical results on convex hulls. A numerical experiment carried out to assess its accuracy, and involving more than 200,000 initializations, shows a failure rate of below 0.8%. ER -