Specifically, the proposed classifier employs left and right projecting vectors to replace the usual high dimensional input weight in the hidden layer to keep the correlative information of the elements, and adopts the idea of neural network with random weights (NNRW) to learn all the parameters.
This projection may distort the projected vector and the secondary task may not adequately be executed.
The uncorrelated entries of the projected vector is obtained due to the orthogonality of the canonical components.
When the projected vector is zero, the vector e falls in the signal subspace and most likely, ω is among the spectral tones.
Then, we select the first 30 components of each projected vector and finally we concatenate them obtaining a vector of 60 components.
Since ( {boldsymbol{K}}_i^r{left {boldsymbol{K}}_i^rright)}^ne boldsymbol{I} ), it will be feasible to compute the minimum reconstruction error between the original vector and projected vector for determining the classification results.
For preparation to derive the equation of motion, we express a projected vector of the lift force on the mid-plane in terms of the direction of the spin angular momentum of a dust grain.
The PAST algorithm for tracking the signal subspace is summarized in Algorithm 1, where x(n) is the new measurement vector at time n, P(n) corresponds to the inverse of the correlation matrix of the projected vector y ( n ) = Û s H ( n ) · x ( n ), which is approximated as y ( n ) = Û s H ( n − 1 ) · x ( n ).
To project vector g onto single scalar, we weight it according to the following logic: because we are interested in the extent of extra-modular connections (which help us identify brokerage opportunities on a meso-level), we sort the GF role categories in the following order, from low to high: (1) coordinator; (2) gatekeeper; (3) representative; (4) itinerant; and (5) liaison.
The initial location (r_{h,i}) for V h can be obtained from its location in the global 3D reconstruction ((r_{3D,h,i})) and corresponding Euler matrix E h,i r_{h,i} = P_{XY} cdot E_{h,i} cdot r_{3D,h,i},h = 1,2, (6 where (P_{XY}) is an operation to project vector to XY plane.
In fact, the frequency content of the spectrum is inversely proportional to the ℓ2-norm of the projected vector: P MU = 1 e H π ⊥ e (58) π ⊥ = ∑ i = k + 1 m v i v i H (59). where v i s are eigenvectors of R corresponding to the noise subspace.
