optidigits

<p>This data set contains vectorised images of handwritten digits (0--9), compressed to 8x8 pixels.</p>

datasets

Implements recently developed projection
pursuit algorithms for finding optimal linear cluster
separators. The clustering algorithms use optimal
hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>;
minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

David Hofmeyr

PPCI

Projection Pursuit for Cluster Identification

optidigits function

<p>A list with entries $x (a 5620x64 matrix with each row corresponding to an image) and $c (a vector of labels indicating the written digit).</p>

optidigits: Optical Recognition of Handwritten Digits

Description

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References