Nonlinear adaptive distance metric learning for clustering

A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensional manifold, where geometric relationships such as pairwise distances are preserved. It can be extended to the nonlinear case by applying the kernel trick, which embeds the data into a feature space by specifying the kernel function that computes the dot products between data points in the feature space. In this paper, we propose a novel unsupervised Nonlinear Adaptive Metric Learning algorithm, called NAML, which performs clustering and distance metric learning simultaneously. NAML first maps the data to a high-dimensional space through a kernel function; then applies a linear projection to find a low-dimensional manifold where the separability of the data is maximized; and finally performs clustering in the low-dimensional space. The performance of NAML depends on the selection of the kernel function and t...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
• Keywords: Data Mining, Distance Metric, KDD 2007, Metric Learning Algorithm, Metric Learning Algorithms