Prototype Vector Machine for Large Scale Semi-Supervised Learning

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Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the ... continued below

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Zhang, Kai; Kwok, James T. & Parvin, Bahram April 29, 2009.

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Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.

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  • 26th International Conference on Machine Learning , Montreal, Canada , June 14-18, 2009

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  • Report No.: LBNL-1953E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 960433
  • Archival Resource Key: ark:/67531/metadc935340

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  • April 29, 2009

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  • Nov. 13, 2016, 7:26 p.m.

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  • Nov. 18, 2016, 4:13 p.m.

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Zhang, Kai; Kwok, James T. & Parvin, Bahram. Prototype Vector Machine for Large Scale Semi-Supervised Learning, article, April 29, 2009; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc935340/: accessed August 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.