On the other hand, it may be more difficult to learn network parameters when the target vector of DAE has a larger dimension.
This presents a challenge for the KWS- data set because the vector of random effects of all the genotypes (g) contains both the vector of effects of the n1 genotyped lines (g1) plus the vector of the effects of the n2 non-genotyped lines (g2) and so has a larger dimension (n1+ n2) than n1.
In comparison with other bases (for example, polynomial bases), one of the advantages of this method is, although the involved matrices have a large dimension, they contain a large percentage of zero entries, which keeps computational effort within reasonable limits.
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Note that a typical microarray data has a large gene wise dimension and can have a large sample wise one in case of combined datasets, Therefore, it can easily be computationally intractable to apply the standard SPM algorithms to the huge-sized gene expression data in practice.
Note that, although the phase space has a large number of dimensions, the schematic diagram is drawn as two-dimensional (climatology and perturbation) by a dimensionality reduction, e.g., the principal component analysis, for ease of understanding.
Merely increasing the backscattering spread to 15° has a large impact: only 22 downlink transmit dimensions can be maintained while suppressing the self-interference to the noise floor 14 of the 36 transmit dimensions must be sacrificed to suppress the self-interference.
If one has a large feature space, say of 60 dimensions, and comparatively few neurons, say 50, then with random initialization those neurons will with high probability not be evenly distributed throughout the feature space.
