Getting to Know the Unknown Unknowns: Destructive-Noise Resistant Boolean Matrix Factorization Article Swipe
Sanjar Karaev
,
Pauli Miettinen
,
Jilles Vreeken
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1137/1.9781611974010.37
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1137/1.9781611974010.37
Finding patterns in binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.MSC codesBoolean matrix factorizationminimum description lengthunknown unknowns
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1137/1.9781611974010.37
- https://epubs.siam.org/doi/pdf/10.1137/1.9781611974010.37
- OA Status
- gold
- Cited By
- 21
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W793192718
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- OpenAlex ID
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https://openalex.org/W793192718Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1137/1.9781611974010.37Digital Object Identifier
- Title
-
Getting to Know the Unknown Unknowns: Destructive-Noise Resistant Boolean Matrix FactorizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-06-30Full publication date if available
- Authors
-
Sanjar Karaev, Pauli Miettinen, Jilles VreekenList of authors in order
- Landing page
-
https://doi.org/10.1137/1.9781611974010.37Publisher landing page
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https://epubs.siam.org/doi/pdf/10.1137/1.9781611974010.37Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://epubs.siam.org/doi/pdf/10.1137/1.9781611974010.37Direct OA link when available
- Concepts
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Factorization, Matrix decomposition, Computer science, Matrix algebra, Matrix (chemical analysis), Non-negative matrix factorization, Noise (video), Algorithm, Artificial intelligence, Materials science, Physics, Image (mathematics), Composite material, Eigenvalues and eigenvectors, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
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2024: 2, 2022: 1, 2020: 4, 2019: 5, 2018: 4Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.this | 118 |
| abstract_inverted_index.when | 58 |
| abstract_inverted_index.with | 183 |
| abstract_inverted_index.(MDL) | 131 |
| abstract_inverted_index.These | 47 |
| abstract_inverted_index.data, | 95 |
| abstract_inverted_index.data: | 66 |
| abstract_inverted_index.equal | 77 |
| abstract_inverted_index.least | 16 |
| abstract_inverted_index.model | 70 |
| abstract_inverted_index.noise | 72, 186 |
| abstract_inverted_index.shows | 177 |
| abstract_inverted_index.well. | 46 |
| abstract_inverted_index.while | 73 |
| abstract_inverted_index.(BMF), | 30 |
| abstract_inverted_index.Length | 130 |
| abstract_inverted_index.Nassau | 179 |
| abstract_inverted_index.adjust | 163 |
| abstract_inverted_index.binary | 3, 86 |
| abstract_inverted_index.common | 102 |
| abstract_inverted_index.dating | 12 |
| abstract_inverted_index.during | 169 |
| abstract_inverted_index.error. | 154 |
| abstract_inverted_index.excels | 180 |
| abstract_inverted_index.length | 146 |
| abstract_inverted_index.levels | 187 |
| abstract_inverted_index.matrix | 28, 209 |
| abstract_inverted_index.mostly | 90 |
| abstract_inverted_index.noise, | 56 |
| abstract_inverted_index.noise. | 83, 92 |
| abstract_inverted_index.recent | 123 |
| abstract_inverted_index.robust | 52 |
| abstract_inverted_index.tiling | 25, 67 |
| abstract_inverted_index.unlike | 157 |
| abstract_inverted_index.Boolean | 27 |
| abstract_inverted_index.Finding | 0 |
| abstract_inverted_index.Minimum | 128 |
| abstract_inverted_index.Nassau, | 140 |
| abstract_inverted_index.address | 117 |
| abstract_inverted_index.against | 53 |
| abstract_inverted_index.amounts | 78 |
| abstract_inverted_index.assumes | 75 |
| abstract_inverted_index.exhibit | 89 |
| abstract_inverted_index.explain | 42 |
| abstract_inverted_index.factors | 165 |
| abstract_inverted_index.instead | 150 |
| abstract_inverted_index.itemset | 18 |
| abstract_inverted_index.mining, | 11 |
| abstract_inverted_index.mining. | 19 |
| abstract_inverted_index.missing | 202 |
| abstract_inverted_index.numbers | 200 |
| abstract_inverted_index.observe | 106, 112 |
| abstract_inverted_index.problem | 8 |
| abstract_inverted_index.removed | 63 |
| abstract_inverted_index.search. | 171 |
| abstract_inverted_index.additive | 71, 80 |
| abstract_inverted_index.approach | 124 |
| abstract_inverted_index.confirms | 194 |
| abstract_inverted_index.data.MSC | 207 |
| abstract_inverted_index.datasets | 182, 193 |
| abstract_inverted_index.directly | 142 |
| abstract_inverted_index.frequent | 17 |
| abstract_inverted_index.however, | 49, 88 |
| abstract_inverted_index.methods, | 48 |
| abstract_inverted_index.patterns | 1, 38 |
| abstract_inverted_index.previous | 159 |
| abstract_inverted_index.problem, | 119 |
| abstract_inverted_index.proposed | 33 |
| abstract_inverted_index.spurious | 114 |
| abstract_inverted_index.unknowns | 213 |
| abstract_inverted_index.Empirical | 172 |
| abstract_inverted_index.addition, | 156 |
| abstract_inverted_index.classical | 7 |
| abstract_inverted_index.datasets, | 87 |
| abstract_inverted_index.employing | 126 |
| abstract_inverted_index.instance, | 97 |
| abstract_inverted_index.introduce | 136 |
| abstract_inverted_index.optimizes | 143 |
| abstract_inverted_index.presence. | 115 |
| abstract_inverted_index.principle | 132 |
| abstract_inverted_index.recently, | 21 |
| abstract_inverted_index.something | 107 |
| abstract_inverted_index.synthetic | 175 |
| abstract_inverted_index.algorithm, | 139 |
| abstract_inverted_index.approaches | 22 |
| abstract_inverted_index.discovered | 168 |
| abstract_inverted_index.evaluation | 173 |
| abstract_inverted_index.hypothesis | 196 |
| abstract_inverted_index.real-world | 85, 192, 206 |
| abstract_inverted_index.relatively | 59 |
| abstract_inverted_index.Description | 129 |
| abstract_inverted_index.algorithms, | 160 |
| abstract_inverted_index.description | 145, 211 |
| abstract_inverted_index.destructive | 55, 82, 91, 185 |
| abstract_inverted_index.non-trivial | 54 |
| abstract_inverted_index.performance | 190 |
| abstract_inverted_index.codesBoolean | 208 |
| abstract_inverted_index.observations | 203 |
| abstract_inverted_index.approximately | 76 |
| abstract_inverted_index.factorization | 29, 149 |
| abstract_inverted_index.lengthunknown | 212 |
| abstract_inverted_index.reconstruction | 153 |
| abstract_inverted_index.presence/absence | 94 |
| abstract_inverted_index.factorizationminimum | 210 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.96740058 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |