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In this paper, we classify the basis elements of the nullZ(W23 v)), derived from the Gaussian elimination on W23 v) (called standard basis), into five classes.
GRSRNMF utilizes the blind source separation technology based on NMF to determine the basis elements (abundances) and their corresponding components (endmembers) without prior knowledge.
In order to learn such dynamics with a collection of basis elements, a control system would generalize correctly and therefore perform optimally if the basis elements that were sensitive to limb velocity were not sensitive to acceleration, and vice versa.
The basis elements located at both sides of the spatial cue are selected as target elements.
The small arrows represent the basis elements of the channel matrix.
In idea 2 (Figure 1, center), the basis elements of the channel matrix, Q, are evenly spaced before the start of the NTF process.
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This may, however, pose a problem when the spatial cue is far from the basis element candidates (Figure 1, left).
For the sake of clarity, we denote by (delta _x) the basis element of (mathbb {K}[X]) corresponding to a point (x in X).
However, idea 1 yields good performance when the spatial cue and the basis element candidates are sufficiently close to each other.
Their positions depend on the values for each channel: for example, the basis element q p = [ 0.5,0.5] T means that the source is coming from the center, and the basis element q p = [ 0.9,0.1] T means that the source is closer to the left channel.
The basis element e is a unit element, henceforth we shall abbreviate e to 1. Also, i, j, k satisfy the following multiplication rule: i^{2}=j^{2}=k^{2}=-1,qquad ij=-ji=k,qquad jk=-kj=i,qquad ki=-ik=j.
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