Sample-dependent graph construction with application to dimensionality reduction

Graph construction plays a key role on learning algorithms based on graph Laplacian. However, the traditional graph construction approaches of -neighborhood and k-nearest-neighbor both need to predefine the same neighbor parameter (or k) for all samples, which usually suffer from the difficulty of parameter selection and generally fail to effectively fit intrinsic structures of data. To mitigate these limitations to a certain extent, in this paper, we present a novel and sample-dependent approach of graph construction, and name the so-constructed graph as Sample-dependent Graph (SG). Specifically, instead of predefining the same neighbor parameter for all samples, the SG depends on samples in question to determine neighbors of each sample and similarities between sample pairs. As a result, it not only avoids the intractability and high expense of neighbor parameter selection but also can more effectively fit intrinsic structures of data. Further, in order to show the effectiveness of...

• Type: Journal
• Year: 2010
• Where: IJON
• Authors: Bo Yang, Songcan Chen
• Keywords: Graph Construction, IJON 2010, Neighbor Parameter, Parameter