The Coding Divergence for Measuring the Complexity of Separating Two Sets

In this paper we integrate two essential processes, discretization of continuous data and learning of a model that explains them, towards fully computational machine learning from continuous data. Discretization is fundamental for machine learning and data mining, since every continuous datum; e.g., a real-valued datum obtained by observation in the real world, must be discretized and converted from analog (continuous) to digital (discrete) form to store in databases. However, most machine learning methods do not pay attention to the situation; i.e., they use digital data in actual applications on a computer whereas assume analog data (usually vectors of real numbers) theoretically. To bridge the gap, we propose a novel measure of the difference between two sets of data, called the coding divergence, and unify two processes discretization and learning computationally. Discretization of continuous data is realized by a topological mapping (in the sense of mathematics) from the d-dimens...

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Mahito Sugiyama, Akihiro Yamamoto
• Keywords: Continuous Data, Discretization, JMLR 2010, Machine