A Study on Handwritten Digits Recognition Using Independent Components
An application of independent component analysis (ICA) to characters recognition is proposed in this paper. The purpose is to evaluate effectiveness of features extracted by ICA. We. propose a novel recognition system that consists of modules for each category. A module has two parts: a feature extraction and a classification. Features are independent components estimated by ICA and outputs of a classification using features are candidates for categories. These candidates are combined based on majority rule and categories are decided for input images. Hand-written digits in MNIST database are used as target characters. FastICA algorithm is applied to these images in order to learn a separating matrix. In recognition experiments, we demonstrated that ICA extracted useful features for handwritten digits and independent components were superior to principal components for the recognition accuracy. Furthermore, we showed the addition of noise pattern to training data was effective for elimination of redundant basis functions, from these results, we confirmed the effectiveness of the feature extraction using ICA.
Manabu Kotani Seiichi Ozawa
Faculty of Engineering Kobe University Kobe, Japan Graduate School of Science and Technology Kobe University Kobe, Japan
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
1658-1663
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）