Visualizing Mercer Kernel Feature Spaces via Kernelized Locally-Linear Embeddings

摘要：

A new technique for projecting highdimensional data to low-dimensional spaces, called locally linear embedding (LLE), has recently been introduced. LLE offers many benefits over traditional alternatives, such as principal component analysis (PCA) and multi-dimensional scaling (MDS). In this paper, we generalize LLE to use Mercer kernels, resulting in a method we call KLLE. Mercer kernels have recently become very popular, due in large part to many recent successes in applying kernel methods such as support vector machines (SVMs) and kernel PCA to many real world problems. KLLE provides a powerful new tool for visualizing how Mercer kernels (implicitly) project data from input space to kernel feature space, which is an open and critical issue for better understanding how kernel methods work and how to best apply them.

作者: Dennis DeCoste

作者单位: Machine Learning Systems Group Jet Propulsion Laboratory / California Institute of Technology M/S 126/347, 4800 Oak Grove Drive, Pasadena, CA 91109, USA

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 905-910

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）