Piecewise linear regression and classification
This work addresses regression and classification problems for researchers and practitioners by offering an incremental extension of linear methods to piecewise linear predictors, useful when integrated into optimization models.
The paper tackles multivariate regression and classification by developing PARC, a method using piecewise linear predictors over polyhedral partitions, which alternates between solving regression/classification problems and assigning points to clusters based on accuracy and separability. It shows that PARC converges to a local minimum and provides a Python implementation, with extensive numerical tests on synthetic and real-world datasets indicating it extends linear methods effectively for use in optimization models.
This paper proposes a method for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space. The resulting algorithm that we call PARC (Piecewise Affine Regression and Classification) alternates between (i) solving ridge regression problems for numeric targets, softmax regression problems for categorical targets, and either softmax regression or cluster centroid computation for piecewise linear separation, and (ii) assigning the training points to different clusters on the basis of a criterion that balances prediction accuracy and piecewise-linear separability. We prove that PARC is a block-coordinate descent algorithm that optimizes a suitably constructed objective function, and that it converges in a finite number of steps to a local minimum of that function. The accuracy of the algorithm is extensively tested numerically on synthetic and real-world datasets, showing that the approach provides an extension of linear regression/classification that is particularly useful when the obtained predictor is used as part of an optimization model. A Python implementation of the algorithm described in this paper is available at http://cse.lab.imtlucca.it/~bemporad/parc .