Abstract
In this chapter, we present representative state-of-the-art unsupervised learning techniques that rely on networked environments to conduct the learning process. In a typical unsupervised task, no external knowledge is presented to the algorithm. As such, the learning process is guided by the provided data, since no prior knowledge about the existing groups is supplied. For network-based methods, the learning procedure is performed by navigating in networks that are constructed from the input data set according to some similarity criterion. As networks naturally embody topological information of data relationships, network-based methods take advantage over algorithms that make use of raw, vector-based data. Moreover, network-based methods can be conceived as a general solution for unsupervised learning tasks even for data sets that are not represented by networks. In this case, we can apply network formation techniques on that data set to generate a network from the input data. Once the network is constructed, all of the network-based techniques described in this chapter can effectively be employed.
Keywords
- Adjacency Matrix
- Community Detection
- Laplacian Matrix
- Nonnegative Matrix Factorization
- Relative Closeness
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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- 1.
See Chap. 4 for a thorough review on network formation methods and similarity functions.
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Silva, T.C., Zhao, L. (2016). Network-Based Unsupervised Learning. In: Machine Learning in Complex Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-17290-3_6
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