Inferring latent task structure for Multitask Learning by Multiple Kernel Learning
Inferring latent task structure for Multitask Learning by Multiple Kernel Learning
复制标题
通过多核学习推断多任务学习的潜在任务结构
作者：
Christian Widmer;Nora C. Toussaint;Y. Altun;G. Rätsch
缺乏足够的训练数据是许多机器学习在计算生物学中的应用的限制因素。如果数据可用于几个不同但相关的问题领域，则可以使用多任务学习算法来基于所有可用信息学习模型。在生物信息学中，许多问题可以通过整合来自多个生物体的数据而纳入多任务学习场景。然而，组合来自多个任务的信息需要仔细考虑任务之间的相似程度。我们提出的方法与多任务学习分类器同时学习或改进任务之间的相似性。这是通过使用最近发表的q-Norm MKL算法，将多任务学习问题表述为多核学习来实现的。结果我们在计算生物学的两个问题上证明了我们的方法的性能。首先，我们证明了我们的方法能够通过改进任务关系来提高具有给定分层任务结构的剪接站点数据集的性能。其次，我们考虑MHC-I数据集，我们假设不知道任务相关性的程度。在这里，我们可以通过多任务分类器从头开始学习任务相似性。在这两种情况下，我们都优于我们比较的基线方法。我们提出了一种新的多任务学习方法，该方法能够与分类器一起学习任务相似性。该框架非常通用，因为它允许在可用的情况下合并有关任务关系的先验知识，但也能够在没有此类先验信息的情况下识别任务相似性。这两种变体在计算生物学的应用中都显示出有希望的结果。
BackgroundThe lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published q-Norm MKL algorithm.ResultsWe demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities ab initio along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against.ConclusionsWe present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.