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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

An optimized unsupervised manifold learning algorithm for manycore architectures

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Author(s):
Baldassin, Alexandro [1] ; Weng, Ying [2] ; Guimaraes Pedronette, Daniel Carlos [1] ; Almeida, Jurandy [3]
Total Authors: 4
Affiliation:
[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp, BR-13506900 Rio Claro, SP - Brazil
[2] Bangor Univ, Sch Comp Sci, Bangor LL57 1UT, Gwynedd - Wales
[3] Univ Fed Sao Paulo UNIFESP, Inst Ciencia & Tecnol, BR-12247014 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: INFORMATION SCIENCES; v. 496, p. 410-430, SEP 2019.
Web of Science Citations: 1
Abstract

Multimedia data, such as images and videos, has become very popular in people's daily life as a result of the widespread use of mobile devices. The ever-increasing amount of such data, along with the necessity for real-time retrieval, has lead to the development of new methods that can process them in a timely fashion with acceptable accuracy. In this paper, we study the performance of ReckNN, an unsupervised manifold learning algorithm based on the reciprocal neighbourhood and the authority of ranked lists. Most of the related work in this field do not fully investigate optimization strategies, an aspect that is becoming more important with the high availability of manycore machines. In order to address that issue, we fully investigate optimization opportunities in this article and make the following three main contributions. Firstly, we develop an efficient and scalable method for storing and accessing the distances between objects (e.g., video or image) based on dictionaries. Secondly, we employ memoization to speed up the computation of authority scores, leading to a significant performance gain even on single-core architectures. Lastly, we devise and implement several parallelization strategies and show that they are scalable on a 72-core Intel machine. The experimental results with MPEG-7, Corel5k and MediaEval benchmarks show that the optimized ReckNN delivers both efficiency and scalability, highlighting the importance of the proposed optimizations for manycore machines. (C) 2018 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 16/06441-7 - Semantic information retrieval in large video databases
Grantee:Jurandy Gomes de Almeida Junior
Support type: Regular Research Grants
FAPESP's process: 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants