Issue No. 09 - Sept. (2013 vol. 35)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.28
Chang-Dong Wang , Sch. of Inf. Sci. & Technol., Sun Yat-sen Univ., Guangzhou, China
Jian-Huang Lai , Sch. of Inf. Sci. & Technol., Sun Yat-sen Univ., Guangzhou, China
Ching Y. Suen , Centre for Pattern Recognition & Machine Intell. (CENPARMI), Concordia Univ., Montreal, QC, Canada
Jun-Yong Zhu , Sch. of Math. & Comput. Sci., Sun Yat-sen Univ., Guangzhou, China
The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP--hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.
Clustering algorithms, Belief propagation, Couplings, Computational modeling, Kernel, Clustering methods, Educational institutions
Chang-Dong Wang, Jian-Huang Lai, C. Y. Suen and Jun-Yong Zhu, "Multi-Exemplar Affinity Propagation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 9, pp. 2223-2237, 2013.