Magnification Control in Self-Organizing Maps and Neural Gas
Magnification Control in Self-Organizing Maps and Neural Gas
复制标题
自组织映射和神经气体中的放大倍数控制
作者：
T. Villmann;J. Claussen
我们考虑了不同的方法来控制自组织映射（SOM）和神经气体（NG）的放大。从矢量量化中放大控制的早期方法开始，我们然后集中在SOM和NG的不同方法上。我们展示了三种结构相似的方法可以应用于两种算法，即局部学习、凹凸学习和赢家放松学习。因此，SOM中的凹凸学习方法扩展到更一般的描述，而NG的凹凸学习是新的。一般来说，两种神经算法相比，控制机制产生的行为只有轻微的不同。然而，我们强调NG的结果对任何数据维度都有效，而在SOM的情况下，结果只对一维情况有效。
We consider different ways to control the magnification in self-organizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show that three structurally similar approaches can be applied to both algorithms that are localized learning, concave-convex learning, and winner-relaxing learning. Thereby, the approach of concave-convex learning in SOM is extended to a more general description, whereas the concave-convex learning for NG is new. In general, the control mechanisms generate only slightly different behavior comparing both neural algorithms. However, we emphasize that the NG results are valid for any data dimension, whereas in the SOM case, the results hold only for the one-dimensional case.
影响因子：
56.9
作者：
KUHL, PK;WILLIAMS, KA;LINDBLOM, B
通讯作者：
LINDBLOM, B