54,276 research outputs found
Robust nearest-neighbor methods for classifying high-dimensional data
We suggest a robust nearest-neighbor approach to classifying high-dimensional
data. The method enhances sensitivity by employing a threshold and truncates to
a sequence of zeros and ones in order to reduce the deleterious impact of
heavy-tailed data. Empirical rules are suggested for choosing the threshold.
They require the bare minimum of data; only one data vector is needed from each
population. Theoretical and numerical aspects of performance are explored,
paying particular attention to the impacts of correlation and heterogeneity
among data components. On the theoretical side, it is shown that our truncated,
thresholded, nearest-neighbor classifier enjoys the same classification
boundary as more conventional, nonrobust approaches, which require finite
moments in order to achieve good performance. In particular, the greater
robustness of our approach does not come at the price of reduced effectiveness.
Moreover, when both training sample sizes equal 1, our new method can have
performance equal to that of optimal classifiers that require independent and
identically distributed data with known marginal distributions; yet, our
classifier does not itself need conditions of this type.Comment: Published in at http://dx.doi.org/10.1214/08-AOS591 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Clustering and classifying images with local and global variability
A procedure for clustering and classifying images determined by three classification
variables is presented. A measure of global variability based on the singular value
decomposition of the image matrices, and two average measures of local variability
based on spatial correlation and spatial changes. The performance of the procedure is
compared using three different databases
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