Efficient Cluster Labeling for Support Vector Clustering

2013 ◽  
Vol 25 (11) ◽  
pp. 2494-2506 ◽  
Author(s):  
V. D'Orangeville ◽  
M. Andre Mayers ◽  
M. Ernest Monga ◽  
M. Shengrui Wang
Keyword(s):  
Support Vector ◽  
Support Vector Clustering ◽  
Cluster Labeling ◽  
Vector Clustering

scholarly journals Maximized Privacy-Preserving Outsourcing on Support Vector Clustering

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 178
Author(s):  
Yuan Ping ◽  
Bin Hao ◽  
Xiali Hei ◽  
Jie Wu ◽  
Baocang Wang
Keyword(s):  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Support Vector ◽  
Convex Decomposition ◽  
Support Vector Clustering ◽  
Cluster Labeling ◽  
Coefficient Vector ◽  
Dual Coordinate Descent ◽  
Secure Protocols ◽  
Vector Clustering

Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a reformative SVC with elementary operations (RSVC-EO, the core of MPPSVC), in which a series of designs make selective outsourcing phase possible. In the training phase, we develop a dual coordinate descent solver, which avoids interactions before getting the encrypted coefficient vector. In the labeling phase, we design a fresh convex decomposition cluster labeling, by which no iteration is required by convex decomposition and no sampling checks exist in connectivity analysis. Afterward, we customize secure protocols to match these operations for essential interactions in the encrypted domain. Considering the privacy-preserving property and efficiency in a semi-honest environment, we proved MPPSVC’s robustness against adversarial attacks. Our experimental results confirm that MPPSVC achieves comparable accuracies to RSVC-EO, which outperforms the state-of-the-art variants of SVC.

Recent Advances in Support Vector Clustering: Theory and Applications

2015 ◽  
Vol 29 (01) ◽  
pp. 1550002 ◽  
Author(s):  
Huina Li ◽  
Yuan Ping
Keyword(s):  
Clustering Algorithm ◽  
Hybrid Methods ◽  
Future Research ◽  
Support Vector ◽  
Research Directions ◽  
Research Issues ◽  
Support Vector Clustering ◽  
Cluster Labeling ◽  
Recent Advances ◽  
Vector Clustering

As an important boundary-based clustering algorithm, support vector clustering (SVC) can benefit many real applications owing to its capability of handling arbitrary cluster shapes, especially those directly or indirectly related to pattern exploration and description. As the application deepens, the importance of performance (i.e. criterions of accuracy and efficiency) of SVC increases. To identify gaps in the current methods and propose novel research directions for SVC, we present a survey of the literature in this area. Our approach is to classify the most recent advances into either theory or application. For theoretical contributions, advances related to parameter selection and optimization, dual-problem solutions, and cluster labeling are introduced. We also simultaneously summarize the advantages and drawbacks of each study. With respect to applications, we clearly describe eight groups of schemes based on SVC, either as individual or hybrid methods. Finally, we identify the gaps in SVC research and suggest several future research issues and trends.

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