scholarly journals Efficient K-Nearest Neighbor Graph Construction Using MapReduce for Large-Scale Data Sets

2014 ◽  
Vol E97.D (12) ◽  
pp. 3142-3154 ◽  
Author(s):  
Tomohiro WARASHINA ◽  
Kazuo AOYAMA ◽  
Hiroshi SAWADA ◽  
Takashi HATTORI
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.


Author(s):  
Bao Bing-Kun ◽  
Yan Shuicheng

Graph-based learning provides a useful approach for modeling data in image annotation problems. In this chapter, the authors introduce how to construct a region-based graph to annotate large scale multi-label images. It has been well recognized that analysis in semantic region level may greatly improve image annotation performance compared to that in whole image level. However, the region level approach increases the data scale to several orders of magnitude and lays down new challenges to most existing algorithms. To this end, each image is firstly encoded as a Bag-of-Regions based on multiple image segmentations. And then, all image regions are constructed into a large k-nearest-neighbor graph with efficient Locality Sensitive Hashing (LSH) method. At last, a sparse and region-aware image-based graph is fed into the multi-label extension of the Entropic graph regularized semi-supervised learning algorithm (Subramanya & Bilmes, 2009). In combination they naturally yield the capability in handling large-scale dataset. Extensive experiments on NUS-WIDE (260k images) and COREL-5k datasets well validate the effectiveness and efficiency of the framework for region-aware and scalable multi-label propagation.


2017 ◽  
Author(s):  
Shirley M. Matteson ◽  
Sonya E. Sherrod ◽  
Sevket Ceyhun Cetin

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