<commit_before># PQSQ-regularized-regression
The method of generalized regression regularization through PQSQ technique is presented. This approach includes lasso, elastic net and ridge regression as partial case.

Current MatLab implementation includes several files:

L1 is majorant function for L_1 norm.

L1_5 is majorant function for L_1 + L_2 norm.

L2 is majorant function for L_2 norm.

LLog is natural logarithm majorant function.

LSqrt is square root or L_0.5 quasi norm majorant function.

PQSQRegularRegr calculates PQSQ regularization of linear regression. See [description]( https://github.com/Mirkes/PQSQ-regularized-regression #Lab1)

PQSQRegularRegrPlot plots coefficient values or goodness of fit of PQSQ regularised regression fits. See [description]( https://github.com/Mirkes/PQSQ-regularized-regression #Lab2)
fastRegularisedRegression perform feature selection for regression which is regularized by Tikhonov regularization (ridge regression) with automated selection of the optimal value of regularization parameter Alpha. See [description]( https://github.com/Mirkes/PQSQ-regularized-regression #Lab3)

<a name="Lab1">Description of PQSQ regularised regression</a>
<pre>
%PQSQRegularRegr calculates PQSQ regularization of linear regression.<commit_msg>Correction of read me<commit_after># PQSQ-regularized-regression
The method of generalized regression regularization through PQSQ technique is presented. This approach includes lasso, elastic net and ridge regression as partial case.

Current MatLab implementation includes several files:

L1 is majorant function for L_1 norm.

L1_5 is majorant function for L_1 + L_2 norm.

L2 is majorant function for L_2 norm.

LLog is natural logarithm majorant function.

LSqrt is square root or L_0.5 quasi norm majorant function.

PQSQRegularRegr calculates PQSQ regularization of linear regression. See [description](https://github.com/Mirkes/PQSQ-regularized-regression#Lab1)

PQSQRegularRegrPlot plots coefficient values or goodness of fit of PQSQ regularised regression fits. See [description]( https://github.com/Mirkes/PQSQ-regularized-regression #Lab2)
fastRegularisedRegression perform feature selection for regression which is regularized by Tikhonov regularization (ridge regression) with automated selection of the optimal value of regularization parameter Alpha. See [description]( https://github.com/Mirkes/PQSQ-regularized-regression #Lab3)

<a name="Lab1">Description of PQSQ regularised regression</a>
<pre>
%PQSQRegularRegr calculates PQSQ regularization of linear regression.
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