Sentence examples for means that matrices from inspiring English sources

Kruskal has demonstrated that the condition (9) is sufficient for uniqueness in CP decomposition [13], where k A is the krank of A. This means that matrices A, B, and C are unique up to permutation and (complex) scaling of their columns, under the Kruskal's condition: k A + k B + k C ≥ 2 R + 2 (9).

P>0 (≥0) means that matrix P is positive definite (positive semidefinite).

For satisfying, denote,, and.,, means that matrix is a positive definite (nonnegative definite) and symmetric matrix.

(A le B) ((A < B)) means that matrix (B - A) is symmetric positive semi-definite (definite).

The notation X > Y ( X ≥ Y ) means that matrix X − Y is positive definite (positive semi-definite, respectively).

This means that matrix dimensions are also fixed, so all the initialization messages from the neighbours must be received in order to start the training step.

Notations Throughout this paper, the superscript 'T' denotes the transpose, and the notation X ≥ Y ( X > Y ) means that matrix X − Y is positive semi-definite (positive definite, respectively).

A≽0 means that matrix A is positive semi-definite, and A ⊥ is the orthonormal basis for the null space of A. A H, ∥A∥, Re[A], Tr[A], Rank[A] and λ max(A) mean the conjugate transpose, the Frobenius norm, the real part, the trace, the rank and the maximal eigenvalue of matrix A, respectively.

For square symmetric matrices X and Y, X < Y means that matrix Y − X is positive-definite.

This means that matrix correlation distance is more appropriate to gesture data with our proposed feature representation.

The deviation of the Cu curve is very obvious and must be discussed in detail, because this means that matrix-independent quantification will fail in some cases.