| Name : R-lolR
| |
| Version : 2.1
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.14.5
| Date : 2024-06-14 11:54:29
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| Group : Development/Libraries/Other
| Source RPM : R-lolR-2.1-lp153.14.5.src.rpm
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| Size : 3.62 MB
| |
| Packager : (none)
| |
| Summary : Linear Optimal Low-Rank Projection
|
Description :
Supervised learning techniques designed for the situation when the
dimensionality exceeds the sample size have a tendency to overfit as
the dimensionality of the data increases. To remedy this High
dimensionality; low sample size (HDLSS) situation, we attempt to learn
a lower-dimensional representation of the data before learning a
classifier. That is, we project the data to a situation where the
dimensionality is more manageable, and then are able to better apply
standard classification or clustering techniques since we will have
fewer dimensions to overfit. A number of previous works have focused on
how to strategically reduce dimensionality in the unsupervised case,
yet in the supervised HDLSS regime, few works have attempted to devise
dimensionality reduction techniques that leverage the labels associated
with the data. In this package and the associated manuscript Vogelstein
et al. (2017) < arXiv:1709.01233>, we provide several methods for
feature extraction, some utilizing labels and some not, along with
easily extensible utilities to simplify cross-validative efforts to
identify the best feature extraction method. Additionally, we include a
series of adaptable benchmark simulations to serve as a standard for
future investigative efforts into supervised HDLSS. Finally, we produce
a comprehensive comparison of the included algorithms across a range of
benchmark simulations and real data applications.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
