Semi-controlled Learning in Information Processing Problems
DOI:
https://doi.org/10.18372/1990-5548.70.16754Keywords:
label propagation, semi-supervised learning, data processing, artificial intelligence, smoothness, manifold, clustering assumptionsAbstract
The article substantiates the need for further research of known methods and the development of new methods of machine learning – semi-supervized learning. It is shown that knowledge of the probability distribution density of the initial data obtained using unlabeled data should carry information useful for deriving the conditional probability distribution density of labels and input data. If this is not the case, semi-supervised learning will not provide any improvement over supervised learning. It may even happen that the use of unlabeled data reduces the accuracy of the prediction. For semi-supervised learning to work, certain assumptions must hold, namely: the semi-supervised smoothness assumption, the clustering assumption (low-density partitioning), and the manifold assumption. A new hybrid semi-supervised learning algorithm using the label propagation method has been developed. An example of using the proposed algorithm is given.
References
O. Chapelle, M. Chi, & A. Zien, (). “A continuation method for semi-supervised SVMs,” in Proceedings of the 23rd international conference on machine learning, 2006a, pp. 185–192. https://doi.org/10.1145/1143844.1143868
X. Zhu, Semi-supervised learning literature survey. Technical Report. 1530, University of Wisconsin-Madison, 2008.
X. Zhu, Semi-supervised learning with graphs. Ph.D. thesis, Carnegie Mellon University, 2005.
A. Subramanya, & P. P. Talukdar, “Graph-based semi-supervised learning,” Synthesis Lectures on Artificial Intelligence and Machine Learning, 8(4), 1–125, 2014. https://doi.org/10.2200/S00590ED1V01Y201408AIM029
I. Triguero, S. García, & F. Herrera, “Self-labeled techniques for semi-supervised learning: Taxonomy, software and empirical study,” Knowledge and Information Systems, 42(2), 245–284, 2015. https://doi.org/10.1007/s10115-013-0706-y
I. Goodfellow, NIPS 2016 tutorial: Generative adversarial networks. arXiv:1701.00160, 2017.
X. Zhu,, & J. Lafferty, “Harmonic mixtures: Combining mixture models and graph-based methods for inductive and scalable semi-supervised learning,” in Proceedings of the 22nd international conference on machine learning. 2005, pp. 1052–1059. ACM. https://doi.org/10.1145/1102351.1102484
Ulrike von Luxburg. "A Tutorial on Spectral Clustering. Statistics and Computing," Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG), 17(4):395–416, 2007. https://doi.org/10.48550/arXiv.0711.0189
Christopher M. Bishop, Pattern Recognition and Machine Learning. Springer, 2007. ISBN-13: 978-0387310732, ISBN-10: 0387310738
Chen, Jie, ren Fang, Haw, and Saad, Yousef, "Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection," Journal of Machine Learning Research, vol.10, pp. 1989–2012, 2009.
Connor, Michael and Kumar, Piyush, "Fast Construction of k-Nearest Neighbor Graphs for Point Clouds," IEEE Trans. Vis. Comput. Graph., 16(4):599–608, 2010. https://doi.org/10.1109/TVCG.2010.9
Dong, Wei, Charikar, Moses, and Li, Kai, "Efficient k-Nearest Neighbor Graph Construction for Generic Similarity Measures," In WWW, pp. 577–586, 2011. https://doi.org/10.1145/1963405.1963487
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf, "Learning with Local and Global Consistency," In NIPS, p. 8, 2003.
Michael Z. Zgurovsky, Viktor M. Sineglazov, Olena I. Chumachenko, Artificial Intelligence Systems Based on Hybrid Neural Networks. Springer https://link.springer.com/book/10.1007/978-3-030-48453-8. Customer can order it via https://www.springer.com/gp/book/9783030484521
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
