Growing topology representing network

• I propose a growing neural network developed from perspective of a generative model.
• This method has a mechanism generating nodes based on information criterion.
• This method finds topologies using connected graph-paths between kernels.

This paper describes a method for finding the topology of a data distribution online using a new growing graph network architecture. Many growing neural networks for finding the topology of data online, such as the Growing Neural Gas, depend on the order and number of input data. For this reason, conventional methods have certain drawbacks: weakness to noise, generating redundant nodes, requiring a great deal of input data, and so on. The proposed method is robust with respect to these issues since it has been developed from the viewpoint of a generative model. This paper presents both the theory and an algorithm in this paper. Moreover, the effectiveness of the proposed method is shown by experiments comparing the proposed method with various growing graph networks.

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