The second-moment matrix is computed in this normalized reference frame and should have an isotropic measure close to one at the final iteration.
12.
|6 = As mentioned above, the square-root of the second-moment matrix defines the transformation matrix that generates the normalized reference frame.
13.
Note that, in this alternative interpretation, a linear regression model forms a basic building block for knowledge representation and is encoded as one moment matrix.
14.
It can be easily shown that applying the fully reverse sweeping to M ( \ vec X ) will recover the initial moment matrix M ( X ).
15.
As a matter of fact, Liu proves that a moment matrix will be recovered through a reverse sweeping after a forward sweeping on the same set of variables.
16.
They also form the basis of the moment matrix representations for the three remaining important cases of linear belief functions, including proper belief functions, linear equations, and linear regression models.
17.
It can also be proved that applying a partial reverse sweeping on X to the matrix M ( \ vec X, Y ) will recover the moment matrix M ( X, Y ).
18.
Using this mathematical framework, the Harris affine detector algorithm iteratively discovers the second-moment matrix that transforms the anisotropic region into a normalized region in which the isotropic measure is sufficiently close to one.
19.
This condition may be approximately fulfilled when the moment matrix of the Gaussian has been adapted for maximum average information to some region of acceptability, because linear transformations of the whole process do not affect efficiency.
20.
When removing a variable, it is important that the variable has not been swept on in the corresponding moment matrix, i . e ., it does not have an arrow sign above the variable.
