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Then the input space can be partitioned.
We then partition the input space into several regions.
The input space is orthogonalized with respect to this measure.
Thus, the dimension of the input space is 441.
Fig. 10 Evaluating identification quality in the input space.
(6) Generate the nonlinear model of the input space. .
Then X approximates the distribution of the input space.
This observation is reasonable since mSVDD learns m hyperspheres in the input space and, hence, is slower than learning only one hypersphere in the input space.
In order to obtain the input-space distance between x and its reconstruction result, it is necessary to map P(φ(x)) back into the input space.
However, the size, or embedding dimension, of the input space was kept constant.
This algorithm builds local linear models by combining PCA with clustering of the input space.
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