Input pruning for neural gas architectures

The neural gas algorithm provides a method to cluster a data space via an adaptive lattice of neurons which captures the topology of the data space. We propose di erent methods to determine the relevance of the single data dimensions for the overall neural architecture. This enables us to perform input pruning for the unsupervised neural gas architecture. The methods are tested on various datasets.

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