Effects of normalization constraints on competitive learning

Implementations of competitive learning often use input and weight vectors "normalized" based on the sum of weight vector components. While it is realized that some distortion of results can occur with this procedure, it is generally not appreciated how dramatic the distortion can be, and that it compromises the dot product as a similarity measure. We show here that in some cases an input vector identical to an existing output node weight vector can be classified as belonging to a different output node. This contradicts the generally-accepted concept of weight vectors developing as prototypes during competitive learning. Ways to minimize this problem are also given.