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The quality of learned features by representation learning determines theperformance of learning algorithms and the related application tasks (such ashigh-dimensional data clustering). As a relatively new paradigm forrepresentation learning, Concept Factorization (CF) has attracted a great dealof interests in the areas of machine learning and data mining for over adecade. Lots of effective CF based methods have been proposed based ondifferent perspectives and properties, but note that it still remains not easyto grasp the essential connections and figure out the underlying explanatoryfactors from exiting studies. In this paper, we therefore survey the recentadvances on CF methodologies and the potential benchmarks by categorizing andsummarizing the current methods. Specifically, we first re-view the root CFmethod, and then explore the advancement of CF-based representation learningranging from shallow to deep/multilayer cases. We also introduce the potentialapplication areas of CF-based methods. Finally, we point out some futuredirections for studying the CF-based representation learning. Overall, thissurvey provides an insightful overview of both theoretical basis and currentdevelopments in the field of CF, which can also help the interested researchersto understand the current trends of CF and find the most appropriate CFtechniques to deal with particular applications.

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