Abstract
To overcome the pricey computation required by redundant kernel function matrix and poor label performance, in a novel perspective, we present support vector clustering with boundary patterns (BPSVC for abbreviation) for efficiency. For the first phase, the conventional method of estimating the support vector function with the whole data is altered by only essential boundary patterns. Thence, BPSVC only need to solve a much simpler optimization problem. For the second phase of cluster labeling, both convex decomposition and cone cluster labeling method are employed by an ensemble labeling strategies for further improvements on accuracy and efficiency. Both theoretical analysis and experimental results show its superiorities in comparison of the state-of-the-art methods, especially for large-scale data analysis.
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Ping, Y., Li, H., Zhang, Y., Zhang, Z. (2013). A New Perspective of Support Vector Clustering with Boundary Patterns. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_20
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DOI: https://doi.org/10.1007/978-3-642-38067-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38066-2
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