Self-measuring Similarity for Multi-task Gaussian Process
Self-measuring Similarity for Multi-task Gaussian Process
复制标题
多任务高斯过程的自测相似度
DOI：
10.1527/tjsai.27.103
复制
发表时间：
2011
期刊：
影响因子：
6.7
通讯作者：
H. Kashima
中科院分区：
文献类型：
--
作者：
K. Hayashi;Takashi Takenouchi;Ryota Tomioka;H. Kashima
多任务学习的目的是在相似的任务之间转移知识。Bonilla等人的多任务高斯过程框架对R任务的C数据点的（不完全）响应进行建模（例如，响应由R × C矩阵给出）;协方差函数定义为输入相关特征的协方差函数与任务间协方差矩阵（根据经验估计为模型参数）的乘积。我们扩展了这个框架，将一个新的相似性度量，它允许更复杂的数据结构的表示。所提出的框架还使我们能够利用额外的信息（例如，输入相关特征），通过构建协方差矩阵并将它们组合在协方差函数上。我们还得到了一个有效的学习算法，通过使用迭代方法进行预测。最后，我们将我们的模型应用到一个真实的数据集的推荐系统，并表明所提出的方法达到了最佳的预测精度的数据集。
Multi-task learning aims at transferring knowledge between similar tasks. The multi-task Gaussian process framework of Bonilla et al. models (incomplete) responses of C data points for R tasks (e.g., the responses are given by R × C matrix) by a Gaussian process; the covariance function is defined as the product of a covariance function on input-dependent features and the inter-task covariance matrix (which is empirically estimated as a model parameter). We extend this framework by incorporating a novel similarity measurement, which allows for the representation of much more complex data structures. The proposed framework also enables us to exploit additional information (e.g., the input-dependent features) by constructing the covariance matrices with combining them on the covariance function. We also derive an efficient learning algorithm to make prediction by using an iterative method. Finally, we apply our model to a real data set of recommender systems and show that the proposed method achieves the best prediction accuracy on the data set.