计算机科学
特征(语言学)
代表(政治)
人工智能
简单(哲学)
特征学习
模式识别(心理学)
过程(计算)
机器学习
数据挖掘
认识论
操作系统
哲学
政治
法学
语言学
政治学
作者
Hyun Ah Song,Sang Yup Lee
标识
DOI:10.1007/978-3-642-42054-2_58
摘要
In this paper, we propose a representation model that demonstrates hierarchical feature learning using nsNMF. We stack simple unit algorithm into several layers to take step-by-step approach in learning. By utilizing NMF as unit algorithm, our proposed network provides intuitive understanding of the feature development process. It is able to represent the underlying structure of feature hierarchies present in complex data in intuitively understandable manner. Experiments with document data successfully discovered feature hierarchies of concepts in data. We also observed that proposed method results in much better classification and reconstruction performance, especially for small number of features.
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