• Journal of KIISE:Computing Practices and Letters
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• 제14권3호
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• pp.296-300
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• 2008

Nonnegative tensor factorization(NTF) is a recent multiway(multilineal) extension of nonnegative matrix factorization(NMF), where nonnegativity constraints are imposed on the CANDECOMP/PARAFAC model. In this paper we consider the Tucker model with nonnegativity constraints and develop a new tensor factorization method, referred to as nonnegative Tucker decomposition (NTD). We derive multiplicative updating algorithms for various discrepancy measures: least square error function, I-divergence, and $\alpha$-divergence.

• 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.

• 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 ${\lambda}$.

• 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.

• Communications of the Korean Mathematical Society
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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.

• Bulletin of the Korean Mathematical Society
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• 제59권1호
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• pp.111-117
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• 2022

Let p_a,b,m(n) be the number of integer partitions of n with more parts congruent to a modulo m than parts congruent to b modulo m. We prove that p_a,b,m(n) ≥ p_b,a,m(n) whenever 1 ≤ a < b ≤ m. We also propose some conjectures concerning series with nonnegative coefficients in their expansions.

• 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.

• Journal of the Korean Mathematical Society
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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.

• Proceedings of the Korean Information Science Society Conference
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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.

• 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.