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Re-weighting
the data: In boosting, we learn
a
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sequence of
simple models. After learning each model,
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we re-weight the
data so that the next model learns to
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deal with the
cases that the previous models found
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difficult.
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There
is a nice guarantee that the overall model
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gets
better.
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Projecting
the data: In PCA, we find the leading
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eigenvector and
then project the data into the
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orthogonal
subspace.
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Distorting
the data: In projection pursuit,
we find a non-
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Gaussian
direction and then distort the data so that it is
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Gaussian along
this direction.
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