• 제목/요약/키워드: nonnegativity

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텐서의 비음수 Tucker 분해 (Nonnegative Tucker Decomposition)

  • 김용덕;최승진
    • 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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    • 제14권3호
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    • pp.296-300
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    • 2008
  • 최근에 개발된 Nonnegative tensor factorization(NTF)는 비음수 행렬 분해(NMF)의 multiway(multilinear) 확장형이다. NTF는 CANDECOMP/PARAFAC 모델에 비음수 제약을 가한 모델이다. 본 논문에서는 Tucker 모델에 비음수 제약을 가한 nonnegative Tucker decomposition(NTD)라는 새로운 텐서 분해 모델을 제안한다. 제안된 NTD 모델을 least squares, I-divergence, $\alpha$-divergence를 이용한 여러 목적함수에 대하여 fitting하는 multiplicative update rule을 유도하였다.

NONNEGATIVITY OF REDUCIBLE SIGN IDEMPOTENT MATRICES

  • Park, Se-Won;Lee, Sang-Gu;Song, Seok-Zuk
    • Journal of applied mathematics & informatics
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    • 제7권2호
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    • pp.665-671
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    • 2000
  • A matrix whose entries consist of the symbols +.- and 0 is called a sign pattern matrix . In 1994 , Eschenbach gave a graph theoretic characterization of irreducible sign idempotent pattern matrices. In this paper, we give a characterization of reducible sign idempotent matrices. We show that reducible sign idempotent matrices, whose digraph is contained in an irreducible sign idempotent matrix, has all nonnegative entries up to equivalences. this extend the previous result.

On some Bounds for the Parameter λ in Steffensen's Inequality

  • PECARIC, JOSIP;KALAMIR, KSENIJA SMOLJAK
    • Kyungpook Mathematical Journal
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    • 제55권4호
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    • pp.969-981
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    • 2015
  • The object is to obtain weaker conditions for the parameter ${\lambda}$ in Steffensen's inequality and its generalizations and refinements additionally assuming nonnegativity of the function f. Furthermore, we contribute to the investigation of the Bellman-type inequalites establishing better bounds for the parameter ${\lambda}$.

Generalized Kullback-Leibler information and its extensions to censored and discrete cases

  • Park, Sangun
    • Journal of the Korean Data and Information Science Society
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    • 제23권6호
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    • pp.1223-1229
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    • 2012
  • In this paper, we propose a generalized Kullback-Leibler (KL) information for measuring the distance between two distribution functions where the extension to the censored case is immediate. The generalized KL information has the nonnegativity and characterization properties, and its censored version has the additional property of monotonic increase. We also extend the discussion to the discrete case and propose a generalized censored measure which is comparable to Pearson's chi-square statistic.

DISTRIBUTIONAL FRACTIONAL POWERS OF SIMILAR OPERATORS WITH APPLICATIONS TO THE BESSEL OPERATORS

  • Molina, Sandra Monica
    • 대한수학회논문집
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    • 제33권4호
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    • pp.1249-1269
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    • 2018
  • This paper provides a method to study the nonnegativity of certain linear operators, from other operators with similar spectral properties. If these new operators are formally self-adjoint and nonnegative, we can study the complex powers using an appropriate locally convex space. In this case, the initial operator also will be nonnegative and we will be able to study its powers. In particular, we have applied this method to Bessel-type operators.

An Algorithm for One-Sided Generalized Least Squares Estimation and Its Application

  • Park, Chul-Gyu
    • Journal of the Korean Statistical Society
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    • 제29권3호
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    • pp.361-373
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    • 2000
  • A simple and efficient algorithm is introduced for generalized least squares estimation under nonnegativity constraints in the components of the parameter vector. This algorithm gives the exact solution to the estimation problem within a finite number of pivot operations. Besides an illustrative example, an empirical study is conducted for investigating the performance of the proposed algorithm. This study indicates that most of problems are solved in a few iterations, and the number of iterations required for optimal solution increases linearly to the size of the problem. Finally, we will discuss the applicability of the proposed algorithm extensively to the estimation problem having a more general set of linear inequality constraints.

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A NEW 3-PARAMETER CURVATURE CONDITION PRESERVED BY RICCI FLOW

  • Gao, Xiang
    • 대한수학회지
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    • 제50권4호
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    • pp.829-845
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    • 2013
  • In this paper, we firstly establish a family of curvature invariant conditions lying between the well-known 2-nonnegative curvature operator and nonnegative curvature operator along the Ricci flow. These conditions are defined by a set of inequalities involving the first four eigenvalues of the curvature operator, which are named as 3-parameter ${\lambda}$-nonnegative curvature conditions. Then a related rigidity property of manifolds with 3-parameter ${\lambda}$-nonnegative curvature operators is also derived. Based on these, we also obtain a strong maximum principle for the 3-parameter ${\lambda}$-nonnegativity along Ricci flow.

정류된 부공간 해석을 이용한 PET 영상 분석 (Rectified Subspace Analysis of Dynamic Positron Emission Tomography)

  • Kim, Sangki;Park, Seungjin;Lee, Jaesung;Lee, Dongsoo
    • 한국정보과학회:학술대회논문집
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    • 한국정보과학회 2002년도 가을 학술발표논문집 Vol.29 No.2 (2)
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    • pp.301-303
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    • 2002
  • Subspace analysis is a popular method for multivariate data analysis and is closely related to factor analysis and principal component analysis (PCA). In the context of image processing (especially positron emission tomography), all data points are nonnegative and it is expected that both basis images and factors are nonnegative in order to obtain reasonable result. In this paper We present a sequential EM algorithm for rectified subspace analysis (subspace in nonnegativity constraint) and apply it to dynamic PET image analysis. Experimental results show that our proposed method is useful in dynamic PET image analysis.

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An Improved Multiplicative Updating Algorithm for Nonnegative Independent Component Analysis

  • Li, Hui;Shen, Yue-Hong;Wang, Jian-Gong
    • ETRI Journal
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    • 제35권2호
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    • pp.193-199
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    • 2013
  • This paper addresses nonnegative independent component analysis (NICA), with the aim to realize the blind separation of nonnegative well-grounded independent source signals, which arises in many practical applications but is hardly ever explored. Recently, Bertrand and Moonen presented a multiplicative NICA (M-NICA) algorithm using multiplicative update and subspace projection. Based on the principle of the mutual correlation minimization, we propose another novel cost function to evaluate the diagonalization level of the correlation matrix, and apply the multiplicative exponentiated gradient (EG) descent update to it to maintain nonnegativity. An efficient approach referred to as the EG-NICA algorithm is derived and its validity is confirmed by numerous simulations conducted on different types of source signals. Results show that the separation performance of the proposed EG-NICA algorithm is superior to that of the previous M-NICA algorithm, with a better unmixing accuracy. In addition, its convergence speed is adjustable by an appropriate user-defined learning rate.