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.

通过表示学习获得的特征的质量决定了学习算法和后续应用任务（例如，高维数据聚类）的性能。作为学习表示的一种有效范例，概念分解（CF）在机器学习和数据挖掘领域已引起了广泛的关注，已有十多年的历史。而且，已经基于不同的观点和性质提出了许多有效的基于CF的方法，但是要掌握必要的联系并从当前的研究中找出潜在的解释性因素仍然不容易。因此，在本文中，我们通过对当前方法进行归类和总结来概述CF方法学的最新进展以及潜在的基准。具体来说，我们首先回顾一下根CF方法，然后探讨从浅层到深层/多层案例的基于CF的表示学习的进展。我们还将介绍基于CF的方法的潜在应用领域。最后，我们指出了研究基于CF的表示学习的未来方向。总体而言，本次调查提供了CF领域的理论基础和当前发展的深刻见解，这也可以帮助感兴趣的研究人员了解CF的当前趋势，并找到最合适的CF技术来应对特定的应用。