Hong Kong Baptist University

About: Hong Kong Baptist University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & China. The organization has 7811 authors who have published 18919 publications receiving 555274 citations. The organization is also known as: Hong Kong Baptist College & HKBU.

MultiRank: co-ranking for objects and relations in multi-relational data

Abstract: The main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i) propose a framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data; (ii) show the existence and uniqueness of such probability distribution so that it can be used for co-ranking for objects and relations very effectively; and (iii) develop an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution. Extensive experiments on real-world data suggest that the proposed framework is able to provide a co-ranking scheme for objects and relations successfully. Experimental results have also shown that our algorithm is computationally efficient, and effective for identification of interesting and explainable co-ranking results.

Parent‐child communications about consumption and advertising in China

Abstract: The current study examines how mainland Chinese parents communicate with their children about consumption and advertising. A survey of 1,665 parents of children aged six to 14 in Beijing, Nanjing and Chengdu was conducted in December 2001 to March 2002. Using Moore and Moschis’s typology of family communication patterns, Chinese parents are classified into four types including laissez‐faire, protective, pluralistic, and consensual parents. Results indicated Chinese parents are classified primarily as consensual in type with both high socio‐ as well as concept‐oriented communication. Family communication patterns differ among parents of different demographic groups as well as among different dyad relationships. Parents with a higher education level and families with a higher household income engaged more frequently in concept‐oriented communication. Pluralistic and consensual parents discussed with children about television commercials more often than laissez‐faire and protective parents. Consensual parents perceived they have a greater influence on children’s attitude toward advertising than laissez‐faire parents. Implication for marketers and advertisers are discussed.

Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition

Abstract: This paper addresses two problems in linear discriminant analysis (LDA) of face recognition. The first one is the problem of recognition of human faces under pose and illumination variations. It is well known that the distribution of face images with different pose, illumination, and face expression is complex and nonlinear. The traditional linear methods, such as LDA, will not give a satisfactory performance. The second problem is the small sample size (S3) problem. This problem occurs when the number of training samples is smaller than the dimensionality of feature vector. In turn, the within-class scatter matrix will become singular. To overcome these limitations, this paper proposes a new kernel machine-based one-parameter regularized Fisher discriminant (K1PRFD) technique. K1PRFD is developed based on our previously developed one-parameter regularized discriminant analysis method and the well-known kernel approach. Therefore, K1PRFD consists of two parameters, namely the regularization parameter and kernel parameter. This paper further proposes a new method to determine the optimal kernel parameter in RBF kernel and regularized parameter in within-class scatter matrix simultaneously based on the conjugate gradient method. Three databases, namely FERET, Yale Group B, and CMU PIE, are selected for evaluation. The results are encouraging. Comparing with the existing LDA-based methods, the proposed method gives superior results.

Housing consumption in urban China: A comparative study of Beijing and Guangzhou

Abstract: The Chinese government has made repeated attempts to end the so-called welfare provision of housing so as to reduce the burden on the state and the individual work units. Development companies have been set up to undertake housing construction and the housing units sold as commodities; these are referred to as ‘commodity housing’. The author conducts a comparative study of housing consumption in Beijing and Guangzhou, drawing upon two surveys of newly completed commodity housing conducted in 1996. In Beijing, which is dominated by the traditional socialist system of economic and social organisation, only a tiny portion of such housing is traded on the open market. In Guangzhou, where many of the market-oriented reform measures were first experimented with, the open market already accounts for a substantial proportion of the newly constructed stock. In both Beijing and Guangzhou, however, the work unit still constitutes the single most important buyer and distributor of commodity housing. Further, if the a...

A New Convex Optimization Model for Multiplicative Noise and Blur Removal

Abstract: The main contribution of this paper is to propose a new convex optimization model for multiplicative noise and blur removal. The main idea is to rewrite a blur and multiplicative noise equation such that both the image variable and the noise variable are decoupled. The resulting objective function involves the total variation regularization term, the term of variance of the inverse of noise, the $\ell_1$-norm of the data-fitting term among the observed image, and noise and image variables. Such a convex minimization model can be solved efficiently by using many numerical methods in the literature. Numerical examples are presented to demonstrate the effectiveness of the proposed model. Experimental results show that the proposed model can handle blur and multiplicative noise (Gamma, Gaussian, or Rayleigh distribution) removal quite well.