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Efficient locality weighted sparse representation for graph-based learning
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DOI：10.1016/j.knosys.2017.01.019.png)
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Abstract
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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., l(1)-graphs) have been proposed in the literature. Although l(1)-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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Sparse representation
Locality preserving
Graph-based learning
Graph construction
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