Learning representations from dendrograms
Artikel i vetenskaplig tidskrift, 2020

We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different distance measures and representations can be inferred from different types of dendrograms, level functions and distance functions. Via an appropriate embedding, we compute a vector-based representation of the inferred distances, in order to enable many numerical machine learning algorithms to employ such distances. Then, to address the model selection problem, we study the aggregation of different dendrogram-based distances respectively in solution space and in representation space in the spirit of deep representations. In the first approach, for example for the clustering problem, we build a graph with positive and negative edge weights according to the consistency of the clustering labels of different objects among different solutions, in the context of ensemble methods. Then, we use an efficient variant of correlation clustering to produce the final clusters. In the second approach, we investigate the combination of different distances and features sequentially in the spirit of multi-layered architectures to obtain the final features. Finally, we demonstrate the effectiveness of our approach via several numerical studies.


Representation learning

Feature extraction

Unsupervised learning

Ensemble method


Morteza Haghir Chehreghani

Chalmers, Data- och informationsteknik, Data Science

Mostafa Haghir Chehreghani

Amirkabir University of Technology

Machine Learning

0885-6125 (ISSN) 1573-0565 (eISSN)

Vol. 109 1779-1802



Datorseende och robotik (autonoma system)

Matematisk analys



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