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A localized linear manifold self-organizing map. (Chinese. English summary) Zbl 1174.68532

Summary: This paper presents a method of localized linear manifold self-organizing map, which is able to learn a set of ordered low-dimensional linear manifolds in the high-dimensional vector space. Compared to state-of-the-art methods based on the method of T. Kohonen et al. [Neural Computation, 9, No. 6, 1321–1344 (1997)], our method avoids confusion of data in the manifold representation. Each neuron in the network approximately learns the mean vector and principal subspace of the data in its local region. The data representation is therefore more discernable. Experiments show that the proposed method performs much better than other three methods in separating clusters. In terms of handwritten digit recognition, the proposed method achieves an accuracy of 98.26% on the training set of the MNISt database and 97.46% on the test set.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68T10 Pattern recognition, speech recognition

Software:

MNIST
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