Convex multi-task feature learning
Convex multi-task feature learning
复制标题
DOI：
10.1007/s10994-007-5040-8
复制
发表时间：
2008-12-01
期刊：
影响因子：
7.5
通讯作者：
Pontil, Massimiliano
中科院分区：
文献类型：
--
作者：
Argyriou, Andreas;Evgeniou, Theodoros;Pontil, Massimiliano
我们提出了一种方法，用于学习多个任务共享的稀疏表示。该方法是著名的单任务1-范数正则化的推广。它基于一种新的非凸正则化器，该正则化器控制任务中常见的学习特征的数量。我们证明了该方法等价于求解一个凸优化问题，其中存在一个收敛到最优解的迭代算法。该算法有一个简单的解释：它交替执行监督和无监督步骤，在前一步中，它学习特定于任务的函数，在后一步中，它学习这些函数的通用跨任务稀疏表示。我们还提供了一个扩展的算法，学习稀疏的非线性表示使用内核。我们报告模拟和真实的数据集上的实验表明，所提出的方法可以提高性能，相对于独立学习每个任务，并导致一些学习功能共同跨相关的任务。作为一种特殊情况，我们的算法也可以用于简单地选择-而不是学习-跨任务的一些常见变量。
We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well-known single-task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learned features common across the tasks. We prove that the method is equivalent to solving a convex optimization problem for which there is an iterative algorithm which converges to an optimal solution. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the former step it learns task-specific functions and in the latter step it learns common-across-tasks sparse representations for these functions. We also provide an extension of the algorithm which learns sparse nonlinear representations using kernels. We report experiments on simulated and real data sets which demonstrate that the proposed method can both improve the performance relative to learning each task independently and lead to a few learned features common across related tasks. Our algorithm can also be used, as a special case, to simply select-not learn-a few common variables across the tasks.