Classification of large scale qualitative data and visualization of their structure.

为了将大型数据集作为各种传感器或文本积累在网络上的信号进行知识发现，即使对于这种大型且不规则的数据集，也需要将具有某些共同属性的组进行分类，作为详细分析的预处理。虽然这样的数据集包含大量的项目，但通常使用少量的项目就足以进行合理的分类。本文主要从理论分析和数值实验两方面研究了定性数据分类的变量选择和正则化方法。这些结果已经应用到利用放置在房间天花板上的红外传感器的数据来估计人类行为的系统中。

To perform knowledge discovery from large data sets as signals from various sensors or texts accumulated in the web, it is required to classify into groups which have some common attribute as a pretreatment for detailed analysis even for such large and irregular data sets. Though such data sets contain a large number of items, it is often sufficient to use a small number of items for reasonable classification. We have studied mainly variable selection and regularization method for classification of qualitative data both from theoretical analysis and numerical experiments. These results have applied to the system for estimating human behavior using the data from the infrared sensors placed in the ceiling of the room.