Unsupervised Learning Algorithm for Signal Separation

被引：0

作者：

Jacob, Theju ^{[1
]}

Snyder, Wesley ^{[1
]}

机构：

[1] North Carolina State Univ, Dept Elect & Comp Engn, Raleigh, NC 27695 USA

来源：

2014 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE FOR MULTIMEDIA, SIGNAL AND VISION PROCESSING (CIMSIVP) | 2014年

关键词：

SELF-ORGANIZATION; VISUAL-CORTEX; RECOGNITION; ORIENTATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a neural network capable of separating inputs in an unsupervised manner. Oja's rule and Self-Organizing map principles are used to construct the network. The network is tested using 1) straight lines 2) MNIST database. The results demonstrate that the network can operate as a general clustering algorithm, with neighboring neurons responding to geometrically similar inputs.

引用

页码：158 / 163

页数：6

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