A Sparse Estimation Method for Multitask Data Following a Poisson Distribution

Abstract

In recent years, in the manufacturing fields, there has been considerable movement for analyzing data obtained from the observation of products and to confirm factors that determine the individual features. For actual analyses, although there is a large number of candidate factors, the number of data whose features are observable is less; consequently, factor inference is often difficult. Lasso is considered to be one of the effective solution to this problem. This study focuses on objects that satisfy the following two conditions: (1) Multiple different features being observed in the same individual, (2) Each feature being represented by a countable value that follows a Poisson distribution. In this study, we extend Lasso to suit the object satisfying these two conditions, formulate and derive a solution algorithm. We demonstrate that the proposed method can estimate the factors more accurately for synthetic data than the existing Lasso.