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Gene-dependent parametrization of Ψ represents a method of regularization allowing us to remove penalty terms in Equations (5)–(7).
One method of regularization, known as 'feature selection', is to select a small subset of features that are sufficient to model the data.
Even when measurement times may be assumed to be concentrated on single time points, our method can be used as a kind of regularization of the standard particle filter method if artificial distributions with highly concentrated masses around the measurement points are introduced.
Also, an iterative method as discretization of regularization method is introduced.
In theoretical results, we have suggested a general filter regularization method of regularized solution (Section 2.2).
For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization.
As a common regularization method, (ell_{1}) regularization ((p=1)) problem has many good properties since it is a convex programming problem.
As a discretization of the regularization method, we propose an iterative method.
As a regularization method, the Tikhonov method has been used to solve ill-posed problems in a number of publications.
It was the first of a series of "regularization programs" that continued over the following decade.
Method of analytical regularization.
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