Coordinate optimization for generalized fused Lasso
Coordinate optimization for generalized fused Lasso
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DOI：
10.1080/03610926.2021.1931888
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Mineaki Ohishi;Keisuke Fukui;K. Okamura;Y. Itoh;Hirokazu Yanagihara
中科院分区：
文献类型：
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作者：
Mineaki Ohishi;Keisuke Fukui;K. Okamura;Y. Itoh;Hirokazu Yanagihara
摘要融合套索是套索的一种扩展，用于缩小参数差异。我们着重于它的一般形式称为广义融合套索（GFL）。GFL的优化问题可以归结为广义Lasso的优化问题，并可以通过广义Lasso的路径算法来求解。此外，路径算法是通过r中的genlasso包实现的。但是，genlasso包存在一些计算问题。然后，我们应用坐标下降算法（CDA）来解决GFL的优化问题。在不考虑Karush-Kuhn-Tucker条件的情况下，给出了封闭形式的CDA更新方程。此外，我们还展示了CDA在实际数据分析中的应用。
Abstract Fused Lasso is one of extensions of Lasso to shrink differences of parameters. We focus on a general form of it called generalized fused Lasso (GFL). The optimization problem for GFL can be came down to that for generalized Lasso and can be solved via a path algorithm for generalized Lasso. Moreover, the path algorithm is implemented via the genlasso package in R. However, the genlasso package has some computational problems. Then, we apply a coordinate descent algorithm (CDA) to solve the optimization problem for GFL. We give update equations of the CDA in closed forms, without considering the Karush-Kuhn-Tucker conditions. Furthermore, we show an application of the CDA to a real data analysis.