By Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue, C.-C. Jay Kuo, Michael S. Lew
This publication constitutes the complaints of the eleventh Pacific Rim convention on Advances in Multimedia details Processing, held in Shanghai in September 2010.
Read or Download Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings PDF
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Additional info for Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings
The 1 -norm regularized logistic regression (L1LR for short) [6,5] has a Bayesian explanation. It uses a prior distribution for β in Equation (1) to have each element in β take on a value at or near zero with a high probability. In L1LR the prior is a Laplace or double exponential prior. Tibishirani  also pointed out that lasso estimates could be interpreted as posterior mode estimates when the regression parameters have Laplace priors . The Bayesian Logistic Regression (BBR) software package has implemented L1LR, which has shown good performance in large scale text classiﬁcation  and for feature selection as well .
Modern Information Retrieval. Addison-Wesley, Reading (1999) 20. : Social Network Analysis: A Handbook, 2nd edn. Sage, Newberry Park (2000) Large Scale Rich Media Information Search 21 21. : Item-based collaborative filtering recommendation algorithms. In: Proc. of Int. Conf. of World Wide Web, pp. 285–295 (2001) 22. : Combination of multiple searches. In: Proc. of the 2nd Text REtrieval Conference (1994) 23. : Building efficient and effective metasearch engines. ACM Comput. Surv. 34(1), 48–89 (2002) 24.
Motivated by above discussion, several potential research directions and critical technical challenges are identified in the domain of rich media information retrieval under social network environment. They include query formulation, recommendation systems, and index structure design for efficiency and scalability improvement. The remainder of this article is organized as below: In Section 2, we present basic concept of query and query formulation. Firstly, two basic ways to formulate query for rich media data are introduced and then we explore main challenges for designing query formulation scheme under social network environment.