A Competitive Wavelet Layer for Pattern Clustering
作者： Roberto Kawakami Harrop Galvão , Takashi Yoneyama
关键词: Pattern recognition 、 Cluster (physics) 、 Layer (object-oriented design) 、 Wavelet 、 Maxima and minima 、 Representation (mathematics) 、 Basis (linear algebra) 、 Wavelet packet decomposition 、 Machine learning 、 Mathematics 、 Artificial intelligence 、 Cluster analysis
摘要: A competitive “wavelet layer” is proposed for pattern clustering. It exploits the representation capabilities of adaptive wavelets to generate template approximations each cluster data. brief review wavelet representations, as well some insight into local minima problems, provided. The method illustrated by a simple clustering problem, in which step responses dynamic systems are discriminated with basis on presence parasitic oscillations. results suggest that layer exhibits superior performance than conventional neural layers when patterns exhibit low signal-to-noise ratio.
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