title: Similarity measure learning in closed-form solution for image classification.
abstract: Adopting a measure is essential in many multimedia applications. Recently, distance learning is becoming an active research problem. In fact, the distance is the natural measure for dissimilarity. Generally, a pairwise relationship between two objects in learning tasks includes two aspects: similarity and dissimilarity. The similarity measure provides different information for pairwise relationships. However, similarity learning has been paid less attention in learning problems. In this work, firstly, we propose a general framework for similarity measure learning (SML). Additionally, we define a generalized type of correlation as a similarity measure. By a set of parameters, generalized correlation provides flexibility for learning tasks. Based on this similarity measure, we present a specific algorithm under the SML framework, called correlation similarity measure learning (CSML), to learn a parameterized similarity measure over input space. A nonlinear extension version of CSML, kernel CSML, is also proposed. Particularly, we give a closed-form solution avoiding iterative search for a local optimal solution in the high-dimensional space as the previous work did. Finally, classification experiments have been performed on face databases and a handwritten digits database to demonstrate the efficiency and reliability of CSML and KCSML.
authors: Faculty of Science and Technology, University of Macau, Taipa 999078, Macau ; Chongqing University, Chongqing 400030, China.
authors: Jing
authors: J
authors: Chen
authors: Faculty of Science and Technology, University of Macau, Taipa 999078, Macau ; Chongqing University, Chongqing 400030, China.
authors: Yuan Yan
authors: YY
authors: Tang
authors: Faculty of Science and Technology, University of Macau, Taipa 999078, Macau.
authors: C L Philip
authors: CL
authors: Chen
authors: Chongqing University, Chongqing 400030, China.
authors: Bin
authors: B
authors: Fang
authors: Chongqing University, Chongqing 400030, China.
authors: Zhaowei
authors: Z
authors: Shang
authors: University of South Carolina, Columbia, SC 29208, USA.
authors: Yuewei
authors: Y
authors: Lin
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical