A novel normalization technique for unsupervised learning in ANN

G Chakraborty, B Chakraborty - IEEE transactions on neural …, 2000 - ieeexplore.ieee.org
IEEE transactions on neural networks, 2000ieeexplore.ieee.org
Unsupervised learning is used to categorize multidimensional data into a number of
meaningful classes on the basis of the similarity or correlation between individual samples.
In neural-network implementation of various unsupervised algorithms such as principal
component analysis, competitive learning or self-organizing map, sample vectors are
normalized to equal lengths so that similarity could be easily and efficiently obtained by their
dot products. In general, sample vectors span the whole multidimensional feature space and …
Unsupervised learning is used to categorize multidimensional data into a number of meaningful classes on the basis of the similarity or correlation between individual samples. In neural-network implementation of various unsupervised algorithms such as principal component analysis, competitive learning or self-organizing map, sample vectors are normalized to equal lengths so that similarity could be easily and efficiently obtained by their dot products. In general, sample vectors span the whole multidimensional feature space and existing normalization methods distort the intrinsic patterns present in the sample set. In this work, a novel method of normalization by mapping the samples to a new space of one more dimension is proposed. The original distribution of the samples in the feature space is shown to be almost preserved in the transformed space. Simple rules are given to map from original space to the normalized space and vice versa.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果

Google学术搜索按钮

example.edu/paper.pdf
搜索
获取 PDF 文件
引用
References