Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
IEEE Transactions on Knowledge and Data Engineering(2021)
摘要
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which h...
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关键词
Convergence,Euclidean distance,Symmetric matrices,Scalability,Computational efficiency,Matrix decomposition
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