Ricardo Fraiman
Resistant estimates for high dimensional and functional data based on random projections.
Tue, 06/14/2011 - 15:01 — SomNambulAuthors: Ricardo Fraiman, Marcela Svarc
Subjects: Methodology
AbstractIn this paper we propose a new robust estimation method based on random
projections which is adaptive, produces an automatic robust estimate, while
being easy to compute for high or infinite dimensional data. Under some
restricted contamination model, the procedure is robust and attains full
efficiency. We challenge the method with some simulation data and we apply it
to a real data example.Interpretable Clustering using Unsupervised Binary Trees.
Tue, 03/29/2011 - 15:01 — SomNambulAuthors: Ricardo Fraiman, Badih Ghattas, Marcela Svarc
Subjects: Methodology
AbstractWe herein introduce a new method of interpretable clustering that uses
unsupervised binary trees. It is a three-stage procedure, the first 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 (joining), similar clusters are joined together, even if
they do not descend from the same node originally.Clustering using Unsupervised Binary Trees: CUBT.
Fri, 11/12/2010 - 16:01 — SomNambulAuthors: Ricardo Fraiman, Badih Ghattas, Marcela Svarc
Subjects: Methodology
AbstractWe introduce a new clustering method based on unsupervised binary trees. It
is a three stages procedure, which performs on a first stage recursive binary
splits reducing the heterogeneity of the data within the new subsamples. On the
second stage (pruning) adjacent nodes are considered to be aggregated. Finally,
on the third stage (joining) similar clusters are joined even if they do not
descend from the same node. Consistency results are obtained and the procedure
is tested on simulated and real data sets- 5 comments
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