Noise-enhanced clustering and competitive learning algorithms

作者：Osoba Osonde; Kosko Bart^*

来源：Neural Networks, 2013, 37: 132-140.

DOI：10.1016/j.neunet.2012.09.012

• Noise injection
• Stochastic resonance
• Clustering
• EM algorithm
• Competitive learning
• k-means clustering

Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning.

• 出版日期2013-1