Supervised Clustering of Persian Handwritten Images Using Regularization and Dimension Reduction Methods

Abstract

Clustering, as a fundamental exploratory data technique, not only is used to discover patterns and structures in complex data set, but also is utilized to group variables in high-dimensional data analysis. Dimension reduction through clustering helps identify important variables and reduce data dimensions without losing significant information. High-dimensional image data sets, such as Persian handwritten images have numerous pixels, making statistical inference difficult. Such high dimensionality property pose challenges for analysis and processing, requiring specialized techniques like clustering to extract information. Incorporating response variable information enhances clustering analysis, transforming it into a supervised method. This article evaluates a supervised clustering approach using Ridge and Lasso penalties, comparing them in analyzing a real data set while identifying important variables. We demonstrate that despite choosing a small number of variables as important variables, Lasso penalty performed relatively well in predicting the labels of new observations for this multi-class data set.