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Maximum variance unfolding

The solution to the maximum variance unfolding problem is found by constructing a Gram matrix, F, whose top eigenvectors give rise to the low-dimensional representation of the data. MVU seeks to maximise llyi yj II with yij e Y, subject to the following constraints... [Pg.13]

Fig. 2.5 Example 2-dimensional embeddings of the S-Curve dataset found by Maximum Variance Unfolding with = 13... Fig. 2.5 Example 2-dimensional embeddings of the S-Curve dataset found by Maximum Variance Unfolding with = 13...
See other pages where Maximum variance unfolding is mentioned: [Pg.13]    [Pg.20]    [Pg.71]    [Pg.75]    [Pg.77]    [Pg.13]    [Pg.20]    [Pg.71]    [Pg.75]    [Pg.77]    [Pg.57]    [Pg.100]   
See also in sourсe #XX -- [ Pg.13 , Pg.77 ]



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Maximum variance

Unfolded

Unfolders

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