Merging Hazy Sets with m-Schemes: A Geometric Approach to Data Visualization
By: Lukas Silvester Barth , Hannaneh Fahimi , Parvaneh Joharinad and more
Potential Business Impact:
Makes complicated data easier to see and understand.
Many machine learning algorithms try to visualize high dimensional metric data in 2D in such a way that the essential geometric and topological features of the data are highlighted. In this paper, we introduce a framework for aggregating dissimilarity functions that arise from locally adjusting a metric through density-aware normalization, as employed in the IsUMap method. We formalize these approaches as m-schemes, a class of methods closely related to t-norms and t-conorms in probabilistic metrics, as well as to composition laws in information theory. These m-schemes provide a flexible and theoretically grounded approach to refining distance-based embeddings.
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