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All-Nearest-Neighbors

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Liang Gu – One of the best experts on this subject based on the ideXlab platform.

  • ICA3PP (1) – Document Nearest Neighbors Query Based on Pairwise Similarity with MapReduce
    Algorithms and Architectures for Parallel Processing, 2018
    Co-Authors: Peipei Lv, Peng Yang, Yongqiang Dong, Liang Gu

    Abstract:

    With the continuous development of Web technology, many Internet issues evolve into Big Data problems, characterized by volume, variety, velocity and variability. Among them, how to organize plenty of web pages and retrieval information needed is a critical one. An important notion is document classification, in which nearest neighbors query is the key issue to be solved. Most parallel nearest neighbors query methods adopt Cartesian Product between training set and testing set resulting in poor time efficiency. In this paper, two methods are proposed on document nearest neighbor query based on pairwise similarity, i.e. brute-force and pre-filtering. brute-force is constituted by two phases (i.e. copying and filtering) and one map-reduce procedure is conducted. In order to obtain nearest neighbors for each document, each document pair is copied twice and all records generated are shuffled. However, time efficiency of shuffle is sensitive to the number of the intermediate results. For the purpose of intermediate results reduction, pre-filtering is proposed for nearest neighbor query based on pairwise similarity. Since only first top-k neighbors are output for each document, the size of records shuffled is kept in the same magnitude as input size in pre-filtering. Additionally, detailed theoretical analysis is provided. The performance of the algorithms is demonstrated by experiments on real world dataset.

  • document nearest neighbors query based on pairwise similarity with mapreduce
    International Conference on Algorithms and Architectures for Parallel Processing, 2018
    Co-Authors: Peipei Lv, Peng Yang, Yongqiang Dong, Liang Gu

    Abstract:

    With the continuous development of Web technology, many Internet issues evolve into Big Data problems, characterized by volume, variety, velocity and variability. Among them, how to organize plenty of web pages and retrieval information needed is a critical one. An important notion is document classification, in which nearest neighbors query is the key issue to be solved. Most parallel nearest neighbors query methods adopt Cartesian Product between training set and testing set resulting in poor time efficiency. In this paper, two methods are proposed on document nearest neighbor query based on pairwise similarity, i.e. brute-force and pre-filtering. brute-force is constituted by two phases (i.e. copying and filtering) and one map-reduce procedure is conducted. In order to obtain nearest neighbors for each document, each document pair is copied twice and all records generated are shuffled. However, time efficiency of shuffle is sensitive to the number of the intermediate results. For the purpose of intermediate results reduction, pre-filtering is proposed for nearest neighbor query based on pairwise similarity. Since only first top-k neighbors are output for each document, the size of records shuffled is kept in the same magnitude as input size in pre-filtering. Additionally, detailed theoretical analysis is provided. The performance of the algorithms is demonstrated by experiments on real world dataset.

A S Morse – One of the best experts on this subject based on the ideXlab platform.

  • coordination of groups of mobile autonomous agents using nearest neighbor rules
    IEEE Transactions on Automatic Control, 2003
    Co-Authors: Ali Jadbabaie, A S Morse

    Abstract:

    In a recent Physical Review Letters article, Vicsek et al. propose a simple but compelling discrete-time model of n autonomous agents (i.e., points or particles) all moving in the plane with the same speed but with different headings. Each agent’s heading is updated using a local rule based on the average of its own heading plus the headings of its “neighbors.” In their paper, Vicsek et al. provide simulation results which demonstrate that the nearest neighbor rule they are studying can cause all agents to eventually move in the same direction despite the absence of centralized coordination and despite the fact that each agent’s set of nearest neighbors change with time as the system evolves. This paper provides a theoretical explanation for this observed behavior. In addition, convergence results are derived for several other similarly inspired models. The Vicsek model proves to be a graphic example of a switched linear system which is stable, but for which there does not exist a common quadratic Lyapunov function.

  • coordination of groups of mobile autonomous agents using nearest neighbor rules
    Conference on Decision and Control, 2002
    Co-Authors: Ali Jadbabaie, Jie Lin, A S Morse

    Abstract:

    Vicsek et al. proposed (1995) a simple but compelling discrete-time model of n autonomous agents {i.e., points or particles} all moving in the plane with the same speed but with different headings. Each agent’s heading is updated using a local rule based on the average of its own heading plus the headings of its “neighbors”. In their paper, Vicsek et al. provide simulation results which demonstrate that the nearest neighbor rule they are studying can cause all agents to eventually move in the same direction despite the absence of centralized coordination and despite the fact that each agent’s set of nearest neighbors change with time as the system evolves. This paper provides a theoretical explanation for this observed behavior. In addition, convergence results are derived for several other similarly inspired models. The Vicsek model proves to be a graphic example of a switched linear system which is stable, but for which there does not exist a common quadratic Lyapunov function.

Jan Kybic – One of the best experts on this subject based on the ideXlab platform.

  • Approximate all nearest neighbor search for high dimensional entropy estimation for image registration
    Signal Processing, 2012
    Co-Authors: Jan Kybic, Ivan Vnučko

    Abstract:

    Information theoretic criteria such as mutual information are often used as similarity measures for inter-modality image registration. For better performance, it is useful to consider vector-valued pixel features. However, this leads to the task of estimating entropy in medium to high dimensional spaces, for which standard histogram entropy estimator is not usable. We have therefore previously proposed to use a nearest neighbor-based Kozachenko-Leonenko (KL) entropy estimator. Here we address the issue of determining a suitable all nearest neighbor (NN) search algorithm for this relatively specific task. We evaluate several well-known state-of-the-art standard algorithms based on k-d trees (FLANN), balanced box decomposition (BBD) trees (ANN), and locality sensitive hashing (LSH), using publicly available implementations. In addition, we present our own method, which is based on k-d trees with several enhancements and is tailored for this particular application. We conclude that all tree-based methods perform acceptably well, with our method being the fastest and most suitable for the all-NN search task needed by the KL estimator on image data, while the ANN and especially FLANN methods being most often the fastest on other types of data. On the other hand, LSH is found the least suitable, with the brute force search being the slowest.

  • ICASSP (3) – Incremental Updating of Nearest Neighbor-Based High-Dimensional Entropy Estimation
    2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 1
    Co-Authors: Jan Kybic

    Abstract:

    We present an algorithm for estimating entropy from high-dimensional data based on Kozachenko-Leonenko nearest neighbor estimator. The problem of finding all nearest neighbors is approximatively solved using a best-bin first (BBF) bottom-up k-D tree traversal. Our main application is evaluating higher-order mutual information (MI) image similarity criteria that, unlike standard scalar MI, are directly usable for vector (e.g. color) images and can take into account neighborhood information. As during the optimization the MI criterion is often evaluated for very similar images, it is advantageous to update the k-D tree incrementally. We show that the resulting algorithm is fast and accurate enough to be practical for the image registration application.