We describe and analyse a new dyadic recursive
partitioning algorithm for efficient
classification of large two-dimensional data sets,
called progressive classification.
classifiers on a low-resolution
representation of the data obtained using the discrete
wavelet transform; in this representation each point
corresponds to a block of samples from the original data.
At each step of the classification process,
the algorithm either decides to classify the
whole block as belonging to a certain class,
or to re-examine the data at a higher-resolution level.
We present simple theoretical results
showing that, compared to traditional sample-by-sample
algorithms, progressive classification is computationally
more efficient and at the same time,
under certain conditions, more accurate.
By: Vittorio Castelli and Ioannis Kontoyiannis
Published in: RC21039 in 1997
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