The k-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to con...
Jing Wang, Jingdong Wang, Gang Zeng, Zhuowen Tu, R...
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...
Locality preserving projections (LPP) is a typical graph-based dimensionality reduction (DR) method, and has been successfully applied in many practical problems such as face recog...
In this paper, we address the scalability issue plaguing graph-based semi-supervised learning via a small number of anchor points which adequately cover the entire point cloud. Cr...
Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumpti...
We present a novel approach to plan recognition in which graph construction and analysis is used as a paradigm. We use a graph structure called a Goal Graph for the plan recogniti...
Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. All graph-based algorithms rely ...
Since the C language imposes little restriction on the use of function pointers, the task of call graph construction for a C program is far more di cult than what the algorithms d...
We present a novel approach to plan recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to rep...
Symbolic analysis shows promise as a foundation for bug-finding, specification inference, verification, and test generation. This paper addresses demand-driven symbolic analysi...