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Yuangang Mei - One of the best experts on this subject based on the ideXlab platform.

  • an efficient registration algorithm based on spin image for lidar 3d point cloud models
    Neurocomputing, 2015
    Co-Authors: Yuangang Mei
    Abstract:

    Abstract Spin image is a good point Feature descriptor of the 3D surface and has been used in model registration for many applications from medical image processing to cooperation of multiple robots. However, researches show that current Spin-Image based Registration (SIR) algorithms present disadvantages in computational efficiency and robustness. Thus in this paper, aiming at 3D model acquired from LiDAR sensor, a new SIR algorithm is proposed to solve these problems. The new algorithm is on the basis of a new-constructed three-Dimensional Feature Space, which, composed of the curvature, the Tsallis entropy of spin image, and the reflection intensity of laser sensor, is combined with the concept of KD-tree to firstly realize the primary key point matching, i.e., to find the Corresponding Point Candidate Set (CPCS). After that, spin-image based corresponding point searching is conducted with respect to each CPCS to precisely obtain the final corresponding points. The most absorbing advantages of the proposed method are as the following two aspects: on one hand, due to the introduction of the extra Features, the fault corresponding relation introduced by spin image based method can be effectively reduced and thus the registration precision and robustness can be improved greatly; on the other hand, the CPCS obtained using low-Dimensional Feature Space and KD-tree reduces extraordinarily the computational burden due to spin-image based correspondence searching. This greatly improves the computational efficiency of the proposed algorithm. Finally, in order to verify the feasibility and validity of the proposed algorithm, experiments are conducted and the results are analyzed.

  • an efficient registration algorithm based on spin image for lidar 3d point cloud models
    Neurocomputing, 2015
    Co-Authors: Yuqing He, Yuangang Mei
    Abstract:

    Spin image is a good point Feature descriptor of the 3D surface and has been used in model registration for many applications from medical image processing to cooperation of multiple robots. However, researches show that current Spin-Image based Registration (SIR) algorithms present disadvantages in computational efficiency and robustness. Thus in this paper, aiming at 3D model acquired from LiDAR sensor, a new SIR algorithm is proposed to solve these problems. The new algorithm is on the basis of a new-constructed three-Dimensional Feature Space, which, composed of the curvature, the Tsallis entropy of spin image, and the reflection intensity of laser sensor, is combined with the concept of MD-tree to firstly realize the primary key point matching, i.e., to find the Corresponding Point Candidate Set (CPCS). After that, spin-image based corresponding point searching is conducted with respect to each CPCS to precisely obtain the final corresponding points. The most absorbing advantages of the proposed method are as the following two aspects: on one hand, due to the introduction of the extra Features, the fault corresponding relation introduced by spin image based method can be effectively reduced and thus the registration precision and robustness can be improved greatly; on the other hand, the CPCS obtained using low-Dimensional Feature Space and MD-tree reduces extraordinarily the computational burden due to spin-image based correspondence searching. This greatly improves the computational efficiency of the proposed algorithm. Finally, in order to verify the feasibility and validity of the proposed algorithm, experiments are conducted and the results are analyzed. (C) 2014 Elsevier B.V. All rights reserved.

Mohammad Javadian - One of the best experts on this subject based on the ideXlab platform.

  • a novel density based fuzzy clustering algorithm for low Dimensional Feature Space
    Fuzzy Sets and Systems, 2017
    Co-Authors: Mohammad Javadian, Saeed Bagheri Shouraki, Soroush Sheikhpour Kourabbaslou
    Abstract:

    Abstract In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the Feature Space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects of all members of each cluster. Finding arbitrary shaped clusters, noise robustness and proposing fuzzy clusters are some of the advantages of our proposed clustering algorithm. The algorithm is described in full details and its performance is evaluated and compared with well-known clustering algorithms on synthetic and real-world datasets.

Jianqing Fan - One of the best experts on this subject based on the ideXlab platform.

  • a selective overview of variable selection in high Dimensional Feature Space
    Statistica Sinica, 2010
    Co-Authors: Jianqing Fan
    Abstract:

    High Dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of pe- nalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high Dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high Dimensional statistical inference. In this article, we present a brief ac- count of the recent developments of theory, methods, and implementations for high Dimensional variable selection. What limits of the Dimensionality such methods can handle, what the role of penalty functions is, and what the statistical properties are rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high Dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high Dimensional variable selection, with emphasis on independence screening and two-scale methods.

  • a selective overview of variable selection in high Dimensional Feature Space invited review article
    arXiv: Statistics Theory, 2009
    Co-Authors: Jianqing Fan
    Abstract:

    High Dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high Dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high Dimensional statistical inference. In this article, we present a brief account of the recent developments of theory, methods, and implementations for high Dimensional variable selection. What limits of the Dimensionality such methods can handle, what the role of penalty functions is, and what the statistical properties are rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high Dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high Dimensional variable selection, with emphasis on independence screening and two-scale methods.

  • sure independence screening for ultrahigh Dimensional Feature Space
    Journal of The Royal Statistical Society Series B-statistical Methodology, 2008
    Co-Authors: Jianqing Fan
    Abstract:

    Summary.  Variable selection plays an important role in high Dimensional statistical modelling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or Dimensionality p, accuracy of estimation and computational cost are two top concerns. Recently, Candes and Tao have proposed the Dantzig selector using L1-regularization and showed that it achieves the ideal risk up to a logarithmic factor  log (p). Their innovative procedure and remarkable result are challenged when the Dimensionality is ultrahigh as the factor  log (p) can be large and their uniform uncertainty principle can fail. Motivated by these concerns, we introduce the concept of sure screening and propose a sure screening method that is based on correlation learning, called sure independence screening, to reduce Dimensionality from high to a moderate scale that is below the sample size. In a fairly general asymptotic framework, correlation learning is shown to have the sure screening property for even exponentially growing Dimensionality. As a methodological extension, iterative sure independence screening is also proposed to enhance its finite sample performance. With dimension reduced accurately from high to below sample size, variable selection can be improved on both speed and accuracy, and can then be accomplished by a well-developed method such as smoothly clipped absolute deviation, the Dantzig selector, lasso or adaptive lasso. The connections between these penalized least squares methods are also elucidated.

  • variable screening in high Dimensional Feature Space
    2007
    Co-Authors: Jianqing Fan
    Abstract:

    Variable selection in high-Dimensional Space characterizes many contemporary problems in scientific discovery and decision making. Fan and Lv [8] introduced the concept of sure screening to reduce the Dimensionality. This article first reviews the part of their ideas and results and then extends them to the likelihood based models. The techniques are then applied to disease classifications in computational biology and portfolio selection in finance.

Soroush Sheikhpour Kourabbaslou - One of the best experts on this subject based on the ideXlab platform.

  • a novel density based fuzzy clustering algorithm for low Dimensional Feature Space
    Fuzzy Sets and Systems, 2017
    Co-Authors: Mohammad Javadian, Saeed Bagheri Shouraki, Soroush Sheikhpour Kourabbaslou
    Abstract:

    Abstract In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the Feature Space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects of all members of each cluster. Finding arbitrary shaped clusters, noise robustness and proposing fuzzy clusters are some of the advantages of our proposed clustering algorithm. The algorithm is described in full details and its performance is evaluated and compared with well-known clustering algorithms on synthetic and real-world datasets.

Peiyi Hao - One of the best experts on this subject based on the ideXlab platform.

  • a new kernel based fuzzy clustering approach support vector clustering with cell growing
    IEEE Transactions on Fuzzy Systems, 2003
    Co-Authors: Junghsien Chiang, Peiyi Hao
    Abstract:

    In this paper, the support vector clustering is extended to an adaptive cell growing model which maps data points to a high Dimensional Feature Space through a desired kernel function. This generalized model is called multiple spheres support vector clustering, which essentially identifies dense regions in the original Space by finding their corresponding spheres with minimal radius in the Feature Space. A multisphere clustering algorithm based on adaptive cluster cell growing method is developed, whereby it is possible to obtain the grade of memberships, as well as cluster prototypes in partition. The effectiveness of the proposed algorithm is demonstrated for the problem of arbitrary cluster shapes and for prototype identification in an actual application to a handwritten digit data set.