Vector norm:
0- norm: the number of non-zero elements in a vector.
1- norm: sum of absolute vector elements, matlab calling function norm (x, 1).
2-norm: Euclid norm (Euclidean norm, the vector used in the calculation length), i.e., the square of the absolute value of the vector elements and re-evolution, matlab calling function norm (x, 2).
∞- norm: that is, all the vector elements in the maximum absolute value, matlab calling function norm (x, inf).
-∞- norm: that is, all the vector elements in the minimum absolute value, matlab calling function norm (x, -inf).
p- norm: i.e., the absolute value of the vector elements and the power p 1 / p power, matlab calling function norm (x, p).
Matrix norm
1- norm: columns and norm, i.e. maximum column vector and matrix for all absolute values, matlab calls the function norm (A, 1).
2-norm:, spectral norm, i.e., square root of the largest eigenvalue of the matrix A'A. matlab calling function norm (x, 2).
∞- norm: row and norm, i.e. vector and the absolute values of the maximum value of all the matrix rows, matlab calls the function norm (A, inf).
F- norm:, Frobenius norm, i.e., an absolute value of sum of squares of the matrix elements to open square, matlab calls the function norm (A, 'fro') .
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are too distribution (Gaussian distribution):
Mean is the mean, standard deviation and RMSE for each number average; a simple example, a set of numbers: (1.1,1.2,1.3,1.4,15), the mean is 1.3, this set of numbers mean that the difference (-0.2, -0.1,0,0.1,0.2) respectively, the number of squared difference (quadratic) respectively (0.04,0.01,0,0.01,0.04), mean square ( the root mean square) is 0.02, the root mean square (square average square root) is 0.141, i.e., the standard deviation is 0.141.
U quasi-normal distribution also known, is a mean of 0 to 1 as the standard deviation of the normal distribution, referred to as N (0,1).