کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946270 | 1439280 | 2017 | 13 صفحه PDF | دانلود رایگان |
Constructing a graph to represent the structure among data objects plays a fundamental role in various data mining tasks with graph-based learning. Since traditional pairwise distance-based graph construction is sensitive to noise and outliers, sparse representation based graphs (e.g., â1-graphs) have been proposed in the literature. Although â1-graphs prove powerful and robust for many graph-based learning tasks, it suffers from weak locality and high computation costs. In this paper, we propose a locality weighted sparse representation (LWSR), which aims for good preservation of the locality structure among data objects and a significant reduction of the computation time. LWSR approximates each object as a sparse linear combination of its nearest neighbors, and weights their corresponding coefficients by their distances to the target object. Experimental results show that LWSR-graph based learning methods outperform state-of-the-art methods in both effectiveness and efficiency for graph-based learning.
Journal: Knowledge-Based Systems - Volume 121, 1 April 2017, Pages 129-141