mlsanityData to 14 Oct 2026

Maximal Ordinal Two-Factorizations

arXiv 2304.03338 ↗First posted 6 Apr 2023cs.AI cs.LG cs.LOcited by 1

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Abstract (arXiv)

Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the incidence relation. In this work, we investigate such ordinal two-factorizations. First, we investigate for formal contexts that omit ordinal two-factorizations the disjointness of the two factors. Then, we show that deciding on the existence of two-factorizations of a given size is an NP-complete problem which makes computing maximal factorizations computationally expensive. Finally, we provide the algorithm Ord2Factor that allows us to compute large ordinal two-factorizations.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 6 Apr 2023.

Builds on

4 of 28 references.

References · 4

  1. 2019Drawing Order Diagrams Through Two-Dimension Extensioncited by 4
  2. 2021Attribute Selection using Contranominal Scalescited by 4
  3. 2023Greedy Discovery of Ordinal Factorscited by 3
  4. 2022Discovering Locally Maximal Bipartite Subgraphscited by 1

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Citing papers per year: 2023: 1.

1 paper.