Abstract
The SIMBAD project puts forward a unified theory of data analysis under a (dis)similarity based object representation framework. Our work builds on the duality of probabilistic and similarity notions on pairwise object comparison. We address the Evidence Accumulation Clustering paradigm as a means of learning pairwise similarity between objects, summarized in a co-association matrix. We show the dual similarity/probabilistic interpretation of the co-association matrix and exploit these for coherent consensus clustering methods, either exploring embeddings over learned pairwise similarities, in an attempt to better highlight the clustering structure of the data, or by means of a unified probabilistic approach leading to soft assignments of objects to clusters.
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Notes
- 1.
Technically, these distances are computed along a graph formed by connecting all k-nearest neighbors.
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Fred, A.L.N. et al. (2013). Learning Similarities from Examples Under the Evidence Accumulation Clustering Paradigm. In: Pelillo, M. (eds) Similarity-Based Pattern Analysis and Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5628-4_5
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