##
**Sparse regression with exact clustering.**
*(English)*
Zbl 1329.62327

Summary: This paper studies a generic sparse regression problem with a customizable sparsity pattern matrix, motivated by, but not limited to, a supervised gene clustering problem in microarray data analysis. The clustered lasso method is proposed with the \(l_{1}\)-type penalties imposed on both the coefficients and their pairwise differences. Somewhat surprisingly, it behaves differently than the lasso or the fused lasso – the exact clustering effect expected from the \(l_{1}\) penalization is rarely seen in applications. An asymptotic study is performed to investigate the power and limitations of the \(l_{1}\)-penalty in sparse regression. We propose to combine data-augmentation and weights to improve the \(l_{1}\) technique. To address the computational issues in high dimensions, we successfully generalize a popular iterative algorithm both in practice and in theory and propose an ‘annealing’ algorithm applicable to generic sparse regressions (including the fused/clustered lasso). Some effective accelerating techniques are further investigated to boost the convergence. The accelerated annealing (AA) algorithm, involving only matrix multiplications and thresholdings, can handle a large design matrix as well as a large sparsity pattern matrix.

### MSC:

62J07 | Ridge regression; shrinkage estimators (Lasso) |

62H30 | Classification and discrimination; cluster analysis (statistical aspects) |

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