A Survey on Concept Factorization: From Shallow to Deep Representation Learning

The quality of obtained features by representation learning determines the performance of a learning algorithm and subsequent application tasks (e.g., high-dimensional data clustering). As an effective paradigm for learning representations, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. Moreover, lots of effective CF-based methods have been proposed based on different perspectives and properties, but it still remains not easy to grasp the essential connections and figure out the underlying explanatory factors from current studies. In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing current methods. Specifically, we first review the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases. We also introduce the potential application areas of CF-based methods. Finally, we point out some future directions for studying the CF-based representation learning. Overall, this survey provides an insightful overview of both theoretical basis and current developments in the field of CF, which can also help the interested researchers to understand the current trends of CF and find the most appropriate CF techniques to deal with particular applications.

Automated summary

The paper discusses the recent advances in CF methodologies and potential benchmarks by categorizing and summarizing current methods. It reviews the root CF method, explores the advancement of CF-based representation learning from shallow to deep/multilayer cases, and introduces potential application areas of CF-based methods. Future directions for studying CF-based representation learning are also discussed, providing insights for researchers to understand current trends and select appropriate CF techniques for specific applications.