Semi-Supervised Vector Quantization for proximity data

Zhu X, Schleif F-M, Hammer B (2013)
In: Proceedings of ESANN 2013. 89-94.

Conference Paper | Published | English

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Abstract
Semi-supervised learning (SSL) is focused on learning from labeled and unlabeled data by incorporating structural and statistical in- formation of the available unlabeled data. The amount of data is dra- matically increasing, but few of them are fully labeled, due to cost and time constraints. This is even more challenging for non-vectorial, proxim- ity data, given by pairwise proximity values. Only few methods provide SSL for this data, limited to positive-semi-definite (psd) data. They also lack interpretable models, which is a relevant aspect in life-sciences where most of these data are found. This paper provides a prototype based SSL approach for proximity data.
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Cite this

Zhu X, Schleif F-M, Hammer B. Semi-Supervised Vector Quantization for proximity data. In: Proceedings of ESANN 2013. 2013: 89-94.
Zhu, X., Schleif, F. - M., & Hammer, B. (2013). Semi-Supervised Vector Quantization for proximity data. Proceedings of ESANN 2013, 89-94.
Zhu, X., Schleif, F. - M., and Hammer, B. (2013). “Semi-Supervised Vector Quantization for proximity data” in Proceedings of ESANN 2013 89-94.
Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Semi-Supervised Vector Quantization for proximity data. In Proceedings of ESANN 2013. pp. 89-94.
X. Zhu, F.-M. Schleif, and B. Hammer, “Semi-Supervised Vector Quantization for proximity data”, Proceedings of ESANN 2013, 2013, pp.89-94.
Zhu, X., Schleif, F.-M., Hammer, B.: Semi-Supervised Vector Quantization for proximity data. Proceedings of ESANN 2013. p. 89-94. (2013).
Zhu, Xibin, Schleif, Frank-Michael, and Hammer, Barbara. “Semi-Supervised Vector Quantization for proximity data”. Proceedings of ESANN 2013. 2013. 89-94.
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