84 research outputs found
Solving -means on High-dimensional Big Data
In recent years, there have been major efforts to develop data stream
algorithms that process inputs in one pass over the data with little memory
requirement. For the -means problem, this has led to the development of
several -approximations (under the assumption that is a
constant), but also to the design of algorithms that are extremely fast in
practice and compute solutions of high accuracy. However, when not only the
length of the stream is high but also the dimensionality of the input points,
then current methods reach their limits.
We propose two algorithms, piecy and piecy-mr that are based on the recently
developed data stream algorithm BICO that can process high dimensional data in
one pass and output a solution of high quality. While piecy is suited for high
dimensional data with a medium number of points, piecy-mr is meant for high
dimensional data that comes in a very long stream. We provide an extensive
experimental study to evaluate piecy and piecy-mr that shows the strength of
the new algorithms.Comment: 23 pages, 9 figures, published at the 14th International Symposium on
Experimental Algorithms - SEA 201
Understanding Human Autonomy Teaming Through Applications
This presentation describes the development and demonstration of human autonomy teaming technologies for improving aviation safety and efficiency during nominal and off-nominal operations by developing and validating increasingly autonomous systems concepts, technologies, and procedures
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