Multiclass Classification and Feature Selection Based on Least Squares Regression with Large Margin Article Swipe
Haifeng Zhao
,
Siqi Wang
,
Zheng Wang
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1162/neco_a_01116
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1162/neco_a_01116
Least squares regression (LSR) is a fundamental statistical analysis technique that has been widely applied to feature learning. However, limited by its simplicity, the local structure of data is easy to neglect, and many methods have considered using orthogonal constraint for preserving more local information. Another major drawback of LSR is that the loss function between soft regression results and hard target values cannot precisely reflect the classification ability; thus, the idea of the large margin constraint is put forward. As a consequence, we pay attention to the concepts of large margin and orthogonal constraint to propose a novel algorithm, orthogonal least squares regression with large margin (OLSLM), for multiclass classification in this letter. The core task of this algorithm is to learn regression targets from data and an orthogonal transformation matrix simultaneously such that the proposed model not only ensures every data point can be correctly classified with a large margin than conventional least squares regression, but also can preserve more local data structure information in the subspace. Our efficient optimization method for solving the large margin constraint and orthogonal constraint iteratively proved to be convergent in both theory and practice. We also apply the large margin constraint in the process of generating a sparse learning model for feature selection via joint [Formula: see text]-norm minimization on both loss function and regularization terms. Experimental results validate that our method performs better than state-of-the-art methods on various real-world data sets.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1162/neco_a_01116
- https://direct.mit.edu/neco/article-pdf/30/10/2781/1046592/neco_a_01116.pdf
- OA Status
- bronze
- Cited By
- 12
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2884224972
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2884224972Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1162/neco_a_01116Digital Object Identifier
- Title
-
Multiclass Classification and Feature Selection Based on Least Squares Regression with Large MarginWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-07-18Full publication date if available
- Authors
-
Haifeng Zhao, Siqi Wang, Zheng WangList of authors in order
- Landing page
-
https://doi.org/10.1162/neco_a_01116Publisher landing page
- PDF URL
-
https://direct.mit.edu/neco/article-pdf/30/10/2781/1046592/neco_a_01116.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://direct.mit.edu/neco/article-pdf/30/10/2781/1046592/neco_a_01116.pdfDirect OA link when available
- Concepts
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Margin (machine learning), Mathematics, Feature selection, Artificial intelligence, Pattern recognition (psychology), Constraint (computer-aided design), Algorithm, Data point, Computer science, Mathematical optimization, Machine learning, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 2, 2022: 2, 2021: 4, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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59Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2097413644, https://openalex.org/W2104294146, https://openalex.org/W1984929054, https://openalex.org/W4239510810, https://openalex.org/W2131987814, https://openalex.org/W4241931738, https://openalex.org/W2123921160, https://openalex.org/W4234698323, https://openalex.org/W2076363162, https://openalex.org/W2036043678, https://openalex.org/W1990529700, https://openalex.org/W2017337590, https://openalex.org/W2111427896, https://openalex.org/W1979089718, https://openalex.org/W2128729667, https://openalex.org/W2119925377, https://openalex.org/W2154053567, https://openalex.org/W2122210511, https://openalex.org/W2053186076, https://openalex.org/W2135046866, https://openalex.org/W2154865814, https://openalex.org/W2043080228, https://openalex.org/W2051674524, https://openalex.org/W1967106605, https://openalex.org/W2004427069, https://openalex.org/W2493525444, https://openalex.org/W1591385104, https://openalex.org/W1480376833, https://openalex.org/W2103333826, https://openalex.org/W2153635508, https://openalex.org/W2171837816, https://openalex.org/W4214747999, https://openalex.org/W19842529, https://openalex.org/W1973948212, https://openalex.org/W4239875977, https://openalex.org/W1607158807, https://openalex.org/W2134665125, https://openalex.org/W2569859441, https://openalex.org/W4248717215, https://openalex.org/W4238306122, https://openalex.org/W2912485311, https://openalex.org/W2472841440, https://openalex.org/W2047028564, https://openalex.org/W3206507930, https://openalex.org/W2155759509, https://openalex.org/W1749992802, https://openalex.org/W2009086942, https://openalex.org/W2128529555, https://openalex.org/W2157751286, https://openalex.org/W3099514962, https://openalex.org/W2752729515, https://openalex.org/W4285719527, https://openalex.org/W2474775652, https://openalex.org/W146900863, https://openalex.org/W2602753196, https://openalex.org/W2119821739, https://openalex.org/W191001584, https://openalex.org/W2069421937, https://openalex.org/W2293618063 |
| referenced_works_count | 59 |
| abstract_inverted_index.a | 5, 81, 97, 149, 204 |
| abstract_inverted_index.As | 80 |
| abstract_inverted_index.We | 192 |
| abstract_inverted_index.an | 128 |
| abstract_inverted_index.be | 145, 185 |
| abstract_inverted_index.by | 20 |
| abstract_inverted_index.in | 111, 166, 187, 199 |
| abstract_inverted_index.is | 4, 28, 50, 77, 120 |
| abstract_inverted_index.of | 26, 48, 72, 89, 117, 202 |
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| abstract_inverted_index.LSR | 49 |
| abstract_inverted_index.Our | 169 |
| abstract_inverted_index.The | 114 |
| abstract_inverted_index.and | 32, 59, 92, 127, 179, 190, 221 |
| abstract_inverted_index.but | 157 |
| abstract_inverted_index.can | 144, 159 |
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| abstract_inverted_index.put | 78 |
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| abstract_inverted_index.also | 158, 193 |
| abstract_inverted_index.been | 12 |
| abstract_inverted_index.both | 188, 218 |
| abstract_inverted_index.core | 115 |
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| abstract_inverted_index.easy | 29 |
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| abstract_inverted_index.hard | 60 |
| abstract_inverted_index.have | 35 |
| abstract_inverted_index.idea | 71 |
| abstract_inverted_index.loss | 53, 219 |
| abstract_inverted_index.many | 33 |
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| abstract_inverted_index.that | 10, 51, 134, 227 |
| abstract_inverted_index.this | 112, 118 |
| abstract_inverted_index.with | 104, 148 |
| abstract_inverted_index.(LSR) | 3 |
| abstract_inverted_index.Least | 0 |
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| abstract_inverted_index.every | 141 |
| abstract_inverted_index.joint | 212 |
| abstract_inverted_index.large | 74, 90, 105, 150, 176, 196 |
| abstract_inverted_index.learn | 122 |
| abstract_inverted_index.least | 101, 154 |
| abstract_inverted_index.local | 24, 43, 162 |
| abstract_inverted_index.major | 46 |
| abstract_inverted_index.model | 137, 207 |
| abstract_inverted_index.novel | 98 |
| abstract_inverted_index.point | 143 |
| abstract_inverted_index.sets. | 239 |
| abstract_inverted_index.thus, | 69 |
| abstract_inverted_index.using | 37 |
| abstract_inverted_index.better | 231 |
| abstract_inverted_index.cannot | 63 |
| abstract_inverted_index.margin | 75, 91, 106, 151, 177, 197 |
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| abstract_inverted_index.method | 172, 229 |
| abstract_inverted_index.proved | 183 |
| abstract_inverted_index.sparse | 205 |
| abstract_inverted_index.target | 61 |
| abstract_inverted_index.terms. | 223 |
| abstract_inverted_index.theory | 189 |
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| abstract_inverted_index.ensures | 140 |
| abstract_inverted_index.feature | 16, 209 |
| abstract_inverted_index.letter. | 113 |
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| abstract_inverted_index.methods | 34, 234 |
| abstract_inverted_index.process | 201 |
| abstract_inverted_index.propose | 96 |
| abstract_inverted_index.reflect | 65 |
| abstract_inverted_index.results | 58, 225 |
| abstract_inverted_index.solving | 174 |
| abstract_inverted_index.squares | 1, 102, 155 |
| abstract_inverted_index.targets | 124 |
| abstract_inverted_index.various | 236 |
| abstract_inverted_index.(OLSLM), | 107 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.ability; | 68 |
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| abstract_inverted_index.concepts | 88 |
| abstract_inverted_index.drawback | 47 |
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| abstract_inverted_index.function | 54, 220 |
| abstract_inverted_index.learning | 206 |
| abstract_inverted_index.neglect, | 31 |
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| abstract_inverted_index.preserve | 160 |
| abstract_inverted_index.proposed | 136 |
| abstract_inverted_index.validate | 226 |
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| abstract_inverted_index.attention | 85 |
| abstract_inverted_index.correctly | 146 |
| abstract_inverted_index.efficient | 170 |
| abstract_inverted_index.learning. | 17 |
| abstract_inverted_index.practice. | 191 |
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| abstract_inverted_index.structure | 25, 164 |
| abstract_inverted_index.subspace. | 168 |
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| abstract_inverted_index.algorithm, | 99 |
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| abstract_inverted_index.considered | 36 |
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| corresponding_author_ids | https://openalex.org/A5102009463, https://openalex.org/A5100401099, https://openalex.org/A5100420087 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I143868143, https://openalex.org/I17145004 |
| citation_normalized_percentile.value | 0.76948379 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |