Feature Extraction Using Independent Components of Each Category
Feature Extraction Using Independent Components of Each Category
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DOI：
10.1007/s11063-004-0634-7
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发表时间：
2005-10
影响因子：
3.1
通讯作者：
M. Kotani;S. Ozawa
中科院分区：
文献类型：
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作者：
M. Kotani;S. Ozawa
我们描述了独立分量分析（伊卡）的模式识别中的应用，以评估伊卡提取的特征的有效性。我们提出了一种识别方法，适用于独立的组件，由模块为每个类别。一个模块有两个部分：特征提取和分类。特征是伊卡估计的独立分量，模块的输出是类别的候选者。这些候选人被合并，类别按照多数规则决定。这种识别方法适用于两个任务：手写数字在MNIST数据库和声学诊断压缩机作为现实世界的任务。该方法采用FastICA算法提取独立特征。通过识别实验，我们证明了伊卡的每一个类别提取有用的功能，这些任务和独立成分是上级优于主成分的识别精度。
We describe an application of independent component analysis (ICA) to pattern recognition in order to evaluate the effectiveness of features extracted by ICA. We propose a recognition method suitable for independent components that consists of modules for each category. A module has two parts: feature extraction and classification. Features are independent components estimated by ICA and outputs of modules are candidates for categories. These candidates are combined and categories are decided with a majority rule. This recognition method is applied to two tasks: hand-written digits in the MNIST database and acoustic diagnosis for a compressor as real-world tasks. A FastICA algorithm is applied to extracting independent features in the proposed method. Through recognition experiments, we demonstrate that the ICA of each category extracts useful features for these tasks and the independent components are superior to the principal components in recognition accuracy.