Name
Abstract | ||
|---|---|---|
During the past few years Boolean matrix factorization (BMF) has become an important direction in data analysis. The minimum description length principle (MDL) was successfully adapted in BMF for the model order selection. Nevertheless, a BMF algorithm performing good results w.r.t. standard measures in BMF is missing. In this paper, we propose a novel from-below Boolean matrix factorization algorithm based on formal concept analysis. The algorithm utilizes the MDL principle as a criterion for the factor selection. On various experiments we show that the proposed algorithm outperforms-from different standpoints-existing state-of-the-art BMF algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11634-019-00383-6 | ADVANCES IN DATA ANALYSIS AND CLASSIFICATION |
Keywords | DocType | Volume |
Boolean matrix factorization, Minimum description length, Factorization quality, Formal concept analysis | Journal | 15 |
Issue | ISSN | Citations |
1 | 1862-5347 | 1 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatiana P. Makhalova | 1 | 3 | 5.10 |
| Martin Trnecka | 2 | 59 | 9.55 |