An Adaptive One-class Classification Algorithm Based on Multi-resolution Minimum Spanning Tree Model in High-dimensional Space
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摘要: 基于最小生成树数据描述(Minimum spanning tree class descriptor, MSTCD)法覆盖半径固定不变,难以形成局部结构紧致性描述. 本文将多尺度分析思想和最小生成树(Minimum spanning tree, MST)结构相结合,提出最小生成树的自适应多分辨率覆盖模型. 该模型利用样本流形的自身结构特点实现对数据的多分辨分析, 任意位置的分辨率由对应的''点''结构和''边''结构共同决定,整体覆盖模型拥有多个覆盖半径, 数据当前位置不同、分辨率不同,实现在多分辨尺度下对数据流形的自适应紧致覆盖. 实验结果表明该方法具有一定的合理性.Abstract: The coverage radii in the algorithm of MSTCD (Minimum spanning tree class descriptor) are generally fixed without diversification, which makes it difficult to construct a close coverage for the local structure. This article combines multi-resolution thought and minimum spanning tree (MST) covering model, and proposes an adaptive multi-resolution covering model based MST in high-dimensional space. In this algorithm, multi-resolution of data is determined by the distributed feature of data manifold itself. The resolution of any location is depended on the structure of sample point and edge. The proposed novel algorithm permits that the whole covering model could have different coverage radii or resolutions and the resolution has relationship with location. Experiments show that the algorithm is reasonable.
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[1] Li W K, Guo Q H, Elkan C. A positive and unlabeled learning algorithm for one-class classification of remote-sensing data. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2): 717-725[2] Mahadevan S, Shah S L. Fault detection and diagnosis in process data using one-class support vector machines. Journal of Process Control, 2009, 19(10): 1627-1639[3] Mena L, Gonzalez J A. Symbolic one-class learning from imbalanced datasets: application in medical diagnosis. International Journal on Artificial Intelligence Tools, 2009, 18(2): 273-309[4] Xu J, Chen Q C, Wang X L, Wei Z Y. One-class classification models for financial industry information recommendation. In: Proceedings of the International Conference on Machine Learning and Cybernetics. Qingdao, China: IEEE, 2010. 3329-3334[5] Gosztolya G, Banhalmi A, Toth L. Using one-class classification techniques in the anti-phoneme problem. In: Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis. Povoa de Varzim, Portugal: Springer, 2009. 433-440[6] Oliveira H, Caeiro J J, Correia P L. Improved road crack detection based on one-class Parzen density estimation and entropy reduction. In: Proceedings of the 17th IEEE International Conference on Image Processing. Hong Kong, China: IEEE, 2010. 2201-2204[7] Choi Y S. Least squares one-class support vector machine. Pattern Recognition Letters, 2009, 30(13): 1236-1240[8] Tian Jiang, Gu Hong. Outlier one class support vector machines. Journal of Electronics and Information Technology, 2010, 32(6): 1284-1288(田江, 顾宏. 孤立点一类支持向量机算法研究. 电子与信息学报, 2010, 32(6): 1284-1288)[9] Gu H, Zhao G Z, Qiu J. One-class support vector machine with relative comparisons. Tsinghua Science and Technology, 2010, 15(2): 190-197[10] Tax D, Duin R. Support vector data description. Machine Learning, 2004, 54(1): 45-56[11] Sakla W, Chan A, Ji J, Sakla A. An SVDD-based algorithm for target detection in hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2): 384-388[12] Huang G X, Chen H F, Yin F. Improved support vector data description. In: Proceedings of the International Conference on Machine Learning and Cybernetics. Qingdao, China: IEEE, 2010. 1459-1463[13] Zhang X L, Ren F. Improving svm learning accuracy with adaboost. In: Proceedings of the 4th International Conference on Natural Computation. Jinan, China: IEEE, 2010. 221-225[14] Juszczak P, Tax D, Pekalska E, Duin R. Minimum spanning tree based one-class classifier. Neurocomputing, 2009, 72(7-9): 1859-1869[15] Hu Zheng-Ping, Xu Cheng-Qian, Jia Qian-Wen. A classification algorithm with reject option based on adaptive minimum spanning tree covering model in high-dimensional space. Journal of Electronics and Information Technology, 2010, 32(12): 2896-2900(胡正平, 许成谦, 贾千文. 基于高维空间最小生成树自适应覆盖模型的可拒绝分类算法. 电子与信息学报, 2010, 32(12): 2896-2900)[16] Wang Shou-Jue. Bionic (topological) pattern recognition — a new model of pattern recognition theory and its applications. Acta Electronica Sinica, 2002, 30(10): 1417-1420(王守觉. 仿生模式识别(拓扑模式识别) — 一种模式识别新模型的理论与应用. 电子学报, 2002, 30(10): 1417-1420)
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