P06-1017 |
covered five relation types . Using
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Isomap
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tool 4 , the 40 instances with
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P13-3017 |
used . The output vectors of the
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ISOMAP
|
algorithm are in 64 dimensions
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W03-0613 |
euclidean distance to do this , where
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Isomap
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operates on prior knowledge of
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N06-4008 |
Analysis , Laplacian Eigenmaps , and
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Isomap
|
. 3.1 Classification Probabilities
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W03-0613 |
in Figure 4 , we can see that
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Isomap
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finds inherent dimensionality
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P13-3017 |
to reduce dimensionality , the
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ISOMAP
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algorithm ( Tenenbaum et al.
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W03-0613 |
used in the scaling . We use the
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Isomap
|
algorithm from ( Tenenbaum et
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W03-0613 |
most music classification tasks .
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Isomap
|
can embed in a set of dimensions
|
W03-0613 |
structure of the audio features .
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Isomap
|
scales dimensions given a NxN
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W03-0613 |
kernel in Equation 1 . We feed D to
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Isomap
|
and ask for a one-dimensional
|
P05-1049 |
examples around A ; ( 2 ) we used
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Isomap
|
to maximally preserve the neighborhood
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W03-0613 |
studying the residual variances of
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Isomap
|
as in Figure 4 , we can see that
|
W10-2802 |
reduction algorithms , such as LLE ,
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ISOMAP
|
, and LTSA , can be modified
|
W10-2801 |
nonlinear reduction algorithms such as
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ISOMAP
|
( Tenenbaum et al. , 2000 ) are
|
W12-5105 |
create a cosine similarity based
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isomap
|
of the corpus-types for the five
|
P15-1009 |
algorithms have been proposed , such as
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ISOMAP
|
( Tenen - baum et al. , 2000
|
W10-2802 |
including Isometric feature mapping (
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ISOMAP
|
) ( Tenenbaum et al. , 2000 )
|