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基于最小包含球的异质空间大数据集快速相似度学习算法

DOI: 10.13195/j.kzyjc.2013.0720, PP. 1553-1561

Keywords: 最小包含球,大数据,异质空间,相似度学习,推荐系统

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Abstract:

针对跨空间数据相似度学习问题提出的跨空间相似度学习(CSAL)算法表现出了良好的性能,并已成功地应用于各类推荐系统中.但构建一个完善的推荐系统,其待处理的数据量常呈现大样本特征,而CSAL算法并不具备大样本快速处理能力.针对此不足,提出了跨空间相似度学习-最小包含球(CSAL-MEB)方法和跨空间相似度学习-核向量机(CSAL-CVM)快速方法.CSAL-CVM方法既具有渐近线性时间复杂度和空间复杂度的优点,同时又继承了CSAL的良好性能.相关实验亦验证了所提出方法的有效性.

References

[1]  Chen J, Ji S, Ceran B, et al. Learning subspace kernels for classification[C]. ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York, 2008: 106-114.
[2]  Cristianini N, Shawe-Taylor J, Elisseeff A, et al. On kerneltarget alignment[C]. Proc of NIPS. Cambridge: MIT Press, 2002: 367-373.
[3]  Qamar A-M, Gaussier E, Chevallet J-P, et al. Similarity learning for nearest neighbor classification[C]. Proc of IEEE ICDM. Pisa, 2008: 983-988.
[4]  Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Trans on Information Systems, 2004, 22(1): 5-53.
[5]  Wang J, Vries A P D, Reinders M J T. Unifying userbased and item-based collaborative filtering approaches by similarity fusion[C]. Proc of SIGIR. New York: ACM Press, 2006: 501-508.
[6]  Paterek A. Improving regularized singular value decomposition for collaborative filtering[C]. Proc of KDD Cup and Workshop. San Jose, 2007: 39-42.
[7]  Koren Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model[C]. ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York, 2008: 426-434.
[8]  Deshpande M, Karypis G. Item-based top-n recommendation algorithms[J]. ACM Trans on Information Systems, 2004, 22(1): 143-177.
[9]  Wu K L, Yu J, Yang M S. A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests[J]. Pattern Recognition Letters, 2005, 26(5): 639-652.
[10]  Yu J, Cheng Q S, Huang H K. Analysis of the weighting exponent in the FCM[J]. IEEE Trans on Systems, Man, and Cybernetics—Part B: Cybernetics, 2004, 34(1): 164-176.
[11]  Yang S, Yan S, Zhang C, et al. Bilinear analysis for kernel selection and nonlinear feature extraction[J]. IEEE Trans on Neural Networks, 2007, 18(5): 1442-1452.
[12]  Deng Z H, Choi K S, Chung F L, et al. Enhanced soft subspace clustering integrating within-cluster and betweencluster information[J]. Pattern Recognition, 2010, 43(3): 767-781.
[13]  蒋亦樟, 邓赵红, 王士同. ML型迁移学习模糊系统[J].自动化学报, 2012, 38(9): 1393-1409.
[14]  (Jiang Y Z, Deng Z H, Wang S T. Mamdani-Larsen type transfer learning fuzzy system[J]. Acta Automatica Sinica, 2012, 38(9): 1393-1409.)
[15]  Badoiu M, Clarkson K L. Optimal core sets for balls computational geometry[J]. Theory and Applications, 2008, 40(1): 14-22.
[16]  Platt J. Fast training of support vector machines using sequential minimal optimization[C]. Advances in Kernel Methods-Support Vector Learning. Cambridge: MIT Press, 2000: 185-208.
[17]  Jarvelin, Kekalainen J. Ir evaluation methods for retrieving highly relevant documents[C]. Proc of SIGIR. New York, 2000: 41-48.
[18]  Weimer M, Karatzoglou A, Le Q V, et al. Cofirankmaximum margin matrix factorization for collaborative ranking[C]. Proc of NIPS. Cambridge: MIT Press, 2007: 1593-1600.
[19]  Tang J H, Qi G J, Zhang L Y, et al. Cross-space affinity learning with its application to movie recommendation[J]. IEEE Trans on Knowledge and Data Engineering, 2011, 25(7): 1510-1519.
[20]  Tsang I, Kwok J, Zurada J. Generalized core vector machines[J]. IEEE Trans on Neural Networks, 2006, 17(5): 1126-1139.
[21]  Tsang I, Kwok J, Cheung P. Core vector machines: Fast SVM training on very large data sets[J]. J of Machine Learning Research, 2005, 6: 363-392.
[22]  Deng Z H, Chung F L, Wang S T. FRSDE:Fast reduced set density estimator using minimal enclosing ball approximation[J]. Pattern Recognition ,2008, 41(4): 1363-1372.
[23]  钱鹏江, 王士同, 邓赵红. 基于最小包含球的大样本数据集快速谱聚类算法[J]. 电子学报, 2010, 38(9): 2035-2041.
[24]  (Qian P J, Wang S T, Deng Z H. Fast spectral clustering for large data sets using minimal enclosing ball[J]. Acta Electronica Sinica, 2010, 38(9): 2035-2041.)
[25]  胡文军, 王士同, 王娟, 等. 一般化最小包含球的大样本快速学习方法[J]. 自动化学报, 2012, 38(11): 1831-1840.
[26]  (Hu W J, Wang S T, Wang J, et al. Fast learning of generalized minimum enclosing ball for large datasets[J]. Acta Automatica Sinica, 2012, 38(11): 1831-1840.)
[27]  Mitchell T. Machine learning, chapter computational learning theory[M]. McGraw-Hill,1997.
[28]  Domeniconi C, Gunopulos D, Ma S, et al. Locally adaptive metrics for clustering high dimensional data[J]. Data Mining and Knowledge Discovery J, 2007, 14(1): 63-97.

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