![]() We discuss the impact of this result on the way dimensionality reduction re-searchers should present their results, and on applicability of dimensionality reduction outside of machine learning. ![]() Our results reveal that, although experts are reasonably consistent in their evaluation of embeddings, novices gener-ally disagree on the quality of an embedding. We also investigate what types of embedding structures humans appreciate a pri-ori. This study investigates whether such human embedding evaluations are reliable, i.e., whether humans tend to agree on the quality of an embedding. When proposing a new technique it is common to simply show rival embeddings side-by-side and let human judgment determine which embed-ding is superior. Means of eval-uating and comparing low-dimensional embeddings useful for visualization, however, are very limited. In the experiments, the well-known Beatles corpus comprising the 180 songs from the twelve official albums is used - adding one album at a time to the collection.Ī cornucopia of dimensionality reduction techniques have emerged over the past decade, leaving data analysts with a wide variety of choices for reducing their data. The different algorithms are experimentally compared based on objective quality measurements as well as in a user study with an interactive user interface. To this end, Growing Self-Organizing Maps, (Landmark) Multidimensional Scaling, Stochastic Neighbor Embedding, and the Neighbor Retrieval Visualizer are considered. ![]() This paper demonstrates to what extent existing approaches are able to incrementally integrate new songs into existing maps and discusses their technical limitations. But how useful are they if the collection is not static but grows over time? Ideally, a map that a user is already familiar with should be altered as little as possible and only as much as necessary to reflect the changes of the underlying collection. Map-based visualizations - sometimes also called projections - are a popular means for exploring music collections. ![]()
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