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We assume that
the data was generated from a
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number of
different classes. The aim is to cluster
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data from the
same class together.
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How
do we decide the number of classes?
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Why
not put each datapoint into a separate class?
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What is the payoff for clustering things
together?
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Clustering
is not a very powerful way to model
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data,
especially if each data-vector can be
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classified
in many different ways? A one-out-
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of-N
classification is not nearly as informative
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as
a feature vector.
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We will
see how to learn feature vectors later.
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