  It is always demanding to learn robust visual representation for various
learning problems; however, this learning and maintenance process usually
suffers from noise, incompleteness or knowledge domain mismatch. Thus, robust
representation learning by removing noisy features or samples, complementing
incomplete data, and mitigating the distribution difference becomes the key.
Along this line of research, low-rank modeling has been widely-applied to
solving representation learning challenges. This survey covers the topic from a
knowledge flow perspective in terms of: (1) robust knowledge recovery, (2)
robust knowledge transfer, and (3) robust knowledge fusion, centered around
several major applications. First of all, we deliver a unified formulation for
robust knowledge discovery given single dataset. Second, we discuss robust
knowledge transfer and fusion given multiple datasets with different knowledge
flows, followed by practical challenges, model variations, and remarks.
Finally, we highlight future research of robust knowledge discovery for
incomplete, unbalance, large-scale data analysis. This would benefit AI
community from literature review to future direction.
