The annual number of publications at scientific venues, for example,
conferences and journals, is growing quickly. Hence, even for researchers
becomes harder and harder to keep track of research topics and their progress.
In this task, researchers can be supported by automated publication analysis.
Yet, many such methods result in uninterpretable, purely numerical
representations. As an attempt to support human analysts, we present
topic space trajectories, a structure that allows for the comprehensible
tracking of research topics. We demonstrate how these trajectories can be
interpreted based on eight different analysis approaches. To obtain
comprehensible results, we employ non-negative matrix factorization as well as
suitable visualization techniques. We show the applicability of our approach on
a publication corpus spanning 50 years of machine learning research from 32
publication venues. Our novel analysis method may be employed for paper
classification, for the prediction of future research topics, and for the
recommendation of fitting conferences and journals for submitting unpublished
Topic Space Trajectories: A case study on machine learning literature