Yeung Dit-Yan, Chang Hong, Dai Guang
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Neural Comput. 2008 Nov;20(11):2839-61. doi: 10.1162/neco.2008.05-07-528.
In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.
近年来，半监督环境下的度量学习引起了大量研究兴趣。一种半监督度量学习利用成对相似性或不相似性约束形式的监督信息。然而，到目前为止提出的大多数方法要么局限于线性度量学习，要么无法随数据集大小良好扩展。在这封信中，我们提出了一种基于核方法的非线性度量学习方法。通过对核矩阵应用低秩逼近，我们的方法可以处理显著更大的数据集。此外，我们的低秩逼近方案可以自然地导致样本外泛化。在人工数据和真实世界数据上进行的实验都显示出非常有前景的结果。
