The time cost and sizes of dimension reduced and original weight matrices for the two experiments are listed in Table 1.
In a general case a set of complex candidate periodic patterns is hidden due to the several dimensions the patterns are defined in and due to a large size of each dimension.
For feature fusion, a new technique is implemented which concentrate on the size of vector dimension.
is the size of one dimension of the squared radar images and is the number of radar images in the set.
The row identifier can be mapped into a concatenation of bits between 0 and k−1, where k is the size of the dimension in the static butterfly topology.
The buffer consists of a single, fixed-size template of dimension m×n, with m equal to the number of gestural parameters (cf., Section 3.1.2), and n equal to the number of samples defining one dance figure (cf., Equation 3).
Similarly, the current absence of black hole detections at the LHC places limits on the maximum size of extra dimensions, and this, in turn, reduces the list of plausible more-than-four-dimensions theories.
In this approach, exponentials are only needed for matrices of small size that depend only on the order of derivatives and number of discretization points, independent of the size of spatial dimensions.
For this reason, it is preferable to optimize the size of both dimensions independently, thus avoiding obtaining biclusters made up of a great numbers of genes and only a few samples.
These methods have the ability to handle large-size problems of dimensions beyond which other methods cease to be efficient.
