Graph weighted subspace learning models in bankruptcy

Many dimensionality reduction algorithms have been proposed easing both tasks of visualization and classification in high dimension problems. Despite the different motivations they can be cast in a graph embedding framework. In this paper we address weighted graph subspace learning methods for bankruptcy analysis. The rationale behind re-embedding the data in a lower dimensional space that would be better filled is twofold: to get the most compact representation (visualization) and to make subsequent processing of data more easy (classification). The approaches used, Graph regularized Non-Negative Matrix Factorization (GNMF) and Spatially Smooth Subspace Learning (SSSL), construct an affinity weight graph matrix to encode geometrical information and to learn in the training set the subspace models that enhance visualization and are able to ease the task of bankruptcy prediction. The experimental results on a real problem of French companies show that from the perspective of financial problem analysis the methodology is quite effective.