  When domain experts are needed to perform data annotation for complex
machine-learning tasks, reducing annotation effort is crucial in order to cut
down time and expenses. For cases when there are no annotations available, one
approach is to utilize the structure of the feature space for clustering-based
active learning (AL) methods. However, these methods are heavily dependent on
how the samples are organized in the feature space and what distance metric is
used. Unsupervised methods such as contrastive predictive coding (CPC) can
potentially be used to learn organized feature spaces, but these methods
typically create high-dimensional features which might be challenging for
estimating data density. In this paper, we combine CPC and multiple
dimensionality reduction methods in search of functioning practices for
clustering-based AL. Our experiments for simulating speech emotion recognition
system deployment show that both the local and global topology of the feature
space can be successfully used for AL, and that CPC can be used to improve
clustering-based AL performance over traditional signal features. Additionally,
we observe that compressing data dimensionality does not harm AL performance
substantially, and that 2-D feature representations achieved similar AL
performance as higher-dimensional representations when the number of
annotations is not very low.
