  The quality of learned features by representation learning determines the
performance of learning algorithms and the related application tasks (such as
high-dimensional data clustering). As a relatively new paradigm for
representation learning, Concept Factorization (CF) has attracted a great deal
of interests in the areas of machine learning and data mining for over a
decade. Lots of effective CF based methods have been proposed based on
different perspectives and properties, but note that it still remains not easy
to grasp the essential connections and figure out the underlying explanatory
factors from exiting studies. In this paper, we therefore survey the recent
advances on CF methodologies and the potential benchmarks by categorizing and
summarizing the current methods. Specifically, we first re-view 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.
