Publicaciones de profesores y profesoras del Departamento de Matemática y Ciencias

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Interpretable Clustering using Unsupervised Binary Trees
(2011) Fraiman, Ricardo; Ghattas, Badih; Svarc, Marcela
We herein introduce a new method of interpretable clustering that uses unsu- pervised binary trees. It is a three-stage procedure, the rst stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (join- ing), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.
Resistant estimates for high dimensional and functional data based on random projections
(2011) Fraiman, Ricardo; Svarc, Marcela
We herein propose a new robust estimation method based on random pro- jections that is adaptive and, automatically produces a robust estimate, while enabling easy computations for high or in nite dimensional data. Under some re- stricted contamination models, the procedure is robust and attains full e ciency. We tested the method using both simulated and real data.