M-ar-K-PCA and M-ar-K-FastICA: Robust Feature Extraction for Classification of Non-Gaussian and Entropic Data Article Swipe
Marianne Lyne Manaog
,
Luca Parisi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1560908/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1560908/v1
The kernel trick has enabled dimensionality reduction techniques to capture a higher extent of non-linearity in the data; however, reproducible, open-source kernels to aid with feature extraction are still limited and are not reliable when projecting features from non-Gaussian and entropic data for a robust classification of data. In fact, traditional kernels are either too simplistic (linear and identity) or have a limited range (sigmoid), thus being unable to handle and transform non-Gaussian and entropic input data adequately. To help in mitigating these challenges, this study presents the m-arcsinh Kernel (’m-ar-K’) Principal Component Analysis (’PCA’) and Fast Independent Component Analysis (’FastICA’) methods for feature extraction, i.e., the ’m-ar-K-PCA’ and ’m-ar-K-FastICA’ respectively. The m-ar-K function, freely available in Python and compatible with its open-source library ’scikit-learn’, is hereby coupled with PCA and FastICA and to achieve more reliable feature extraction in presence of a high extent of non-Gaussianity and randomness in the data, reducing the need for standardisation and pre-whitening respectively. Different classification tasks were considered, as related to five (N = 5) open access datasets of various degrees of non-normality and information entropy, available from scikit-learn and the University California Irvine (UCI) Machine Learning repository. Experimental results demonstrate improvements in the classification performance brought by the proposed feature extraction techniques. The novel m-ar-K-PCA and m-ar-K-FastICA dimensionality reduction approaches are compared to the ’PCA’ and ’FastICA’ gold standard methods respectively, supporting their overall higher reliability and computational efficiency, regardless of the underlying non-Gaussianity and uncertainty in the data. Thus, in particular, m-ar-K-PCA extends the use of PCA to pseudo-Gaussian distributions and m-ar-K-FastICA enables FastICA to withstand not only highly non-normal distributions, but also entropic data.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1560908/v1
- https://www.researchsquare.com/article/rs-1560908/latest.pdf
- OA Status
- green
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281788376
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281788376Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1560908/v1Digital Object Identifier
- Title
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M-ar-K-PCA and M-ar-K-FastICA: Robust Feature Extraction for Classification of Non-Gaussian and Entropic DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-06Full publication date if available
- Authors
-
Marianne Lyne Manaog, Luca ParisiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1560908/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-1560908/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-1560908/latest.pdfDirect OA link when available
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FastICA, Pattern recognition (psychology), Gaussian, Artificial intelligence, Feature extraction, Mathematics, Computer science, Chemistry, Blind signal separation, Computational chemistry, Computer network, Channel (broadcasting)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.use | 254 |
| abstract_inverted_index.Fast | 97 |
| abstract_inverted_index.also | 272 |
| abstract_inverted_index.data | 42, 77 |
| abstract_inverted_index.five | 169 |
| abstract_inverted_index.from | 38, 185 |
| abstract_inverted_index.gold | 226 |
| abstract_inverted_index.have | 61 |
| abstract_inverted_index.help | 80 |
| abstract_inverted_index.high | 144 |
| abstract_inverted_index.more | 136 |
| abstract_inverted_index.need | 155 |
| abstract_inverted_index.only | 267 |
| abstract_inverted_index.open | 173 |
| abstract_inverted_index.this | 85 |
| abstract_inverted_index.thus | 66 |
| abstract_inverted_index.were | 164 |
| abstract_inverted_index.when | 35 |
| abstract_inverted_index.with | 25, 121, 129 |
| abstract_inverted_index.(UCI) | 192 |
| abstract_inverted_index.Thus, | 248 |
| abstract_inverted_index.being | 67 |
| abstract_inverted_index.data, | 152 |
| abstract_inverted_index.data. | 48, 247, 274 |
| abstract_inverted_index.data; | 18 |
| abstract_inverted_index.fact, | 50 |
| abstract_inverted_index.i.e., | 106 |
| abstract_inverted_index.input | 76 |
| abstract_inverted_index.novel | 212 |
| abstract_inverted_index.range | 64 |
| abstract_inverted_index.still | 29 |
| abstract_inverted_index.study | 86 |
| abstract_inverted_index.tasks | 163 |
| abstract_inverted_index.their | 231 |
| abstract_inverted_index.these | 83 |
| abstract_inverted_index.trick | 3 |
| abstract_inverted_index.Irvine | 191 |
| abstract_inverted_index.Kernel | 90 |
| abstract_inverted_index.Python | 118 |
| abstract_inverted_index.access | 174 |
| abstract_inverted_index.either | 54 |
| abstract_inverted_index.extent | 13, 145 |
| abstract_inverted_index.freely | 115 |
| abstract_inverted_index.handle | 70 |
| abstract_inverted_index.hereby | 127 |
| abstract_inverted_index.higher | 12, 233 |
| abstract_inverted_index.highly | 268 |
| abstract_inverted_index.kernel | 2 |
| abstract_inverted_index.m-ar-K | 113 |
| abstract_inverted_index.robust | 45 |
| abstract_inverted_index.unable | 68 |
| abstract_inverted_index.(linear | 57 |
| abstract_inverted_index.FastICA | 132, 263 |
| abstract_inverted_index.Machine | 193 |
| abstract_inverted_index.achieve | 135 |
| abstract_inverted_index.brought | 204 |
| abstract_inverted_index.capture | 10 |
| abstract_inverted_index.coupled | 128 |
| abstract_inverted_index.degrees | 178 |
| abstract_inverted_index.enabled | 5 |
| abstract_inverted_index.enables | 262 |
| abstract_inverted_index.extends | 252 |
| abstract_inverted_index.feature | 26, 104, 138, 208 |
| abstract_inverted_index.kernels | 22, 52 |
| abstract_inverted_index.library | 124 |
| abstract_inverted_index.limited | 30, 63 |
| abstract_inverted_index.methods | 102, 228 |
| abstract_inverted_index.overall | 232 |
| abstract_inverted_index.related | 167 |
| abstract_inverted_index.results | 197 |
| abstract_inverted_index.various | 177 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Analysis | 94, 100 |
| abstract_inverted_index.Learning | 194 |
| abstract_inverted_index.compared | 220 |
| abstract_inverted_index.datasets | 175 |
| abstract_inverted_index.entropic | 41, 75, 273 |
| abstract_inverted_index.entropy, | 183 |
| abstract_inverted_index.features | 37 |
| abstract_inverted_index.however, | 19 |
| abstract_inverted_index.presence | 141 |
| abstract_inverted_index.presents | 87 |
| abstract_inverted_index.proposed | 207 |
| abstract_inverted_index.reducing | 153 |
| abstract_inverted_index.reliable | 34, 137 |
| abstract_inverted_index.standard | 227 |
| abstract_inverted_index.Component | 93, 99 |
| abstract_inverted_index.Different | 161 |
| abstract_inverted_index.Principal | 92 |
| abstract_inverted_index.available | 116, 184 |
| abstract_inverted_index.function, | 114 |
| abstract_inverted_index.identity) | 59 |
| abstract_inverted_index.m-arcsinh | 89 |
| abstract_inverted_index.reduction | 7, 217 |
| abstract_inverted_index.transform | 72 |
| abstract_inverted_index.withstand | 265 |
| abstract_inverted_index.’PCA’ | 223 |
| abstract_inverted_index.(sigmoid), | 65 |
| abstract_inverted_index.California | 190 |
| abstract_inverted_index.University | 189 |
| abstract_inverted_index.approaches | 218 |
| abstract_inverted_index.compatible | 120 |
| abstract_inverted_index.extraction | 27, 139, 209 |
| abstract_inverted_index.m-ar-K-PCA | 213, 251 |
| abstract_inverted_index.mitigating | 82 |
| abstract_inverted_index.non-normal | 269 |
| abstract_inverted_index.projecting | 36 |
| abstract_inverted_index.randomness | 149 |
| abstract_inverted_index.regardless | 238 |
| abstract_inverted_index.simplistic | 56 |
| abstract_inverted_index.supporting | 230 |
| abstract_inverted_index.techniques | 8 |
| abstract_inverted_index.underlying | 241 |
| abstract_inverted_index.(’PCA’) | 95 |
| abstract_inverted_index.Independent | 98 |
| abstract_inverted_index.adequately. | 78 |
| abstract_inverted_index.challenges, | 84 |
| abstract_inverted_index.considered, | 165 |
| abstract_inverted_index.demonstrate | 198 |
| abstract_inverted_index.efficiency, | 237 |
| abstract_inverted_index.extraction, | 105 |
| abstract_inverted_index.information | 182 |
| abstract_inverted_index.open-source | 21, 123 |
| abstract_inverted_index.particular, | 250 |
| abstract_inverted_index.performance | 203 |
| abstract_inverted_index.reliability | 234 |
| abstract_inverted_index.repository. | 195 |
| abstract_inverted_index.techniques. | 210 |
| abstract_inverted_index.traditional | 51 |
| abstract_inverted_index.uncertainty | 244 |
| abstract_inverted_index.Experimental | 196 |
| abstract_inverted_index.improvements | 199 |
| abstract_inverted_index.non-Gaussian | 39, 73 |
| abstract_inverted_index.scikit-learn | 186 |
| abstract_inverted_index.computational | 236 |
| abstract_inverted_index.distributions | 259 |
| abstract_inverted_index.non-linearity | 15 |
| abstract_inverted_index.non-normality | 180 |
| abstract_inverted_index.pre-whitening | 159 |
| abstract_inverted_index.reproducible, | 20 |
| abstract_inverted_index.respectively, | 229 |
| abstract_inverted_index.respectively. | 111, 160 |
| abstract_inverted_index.’FastICA’ | 225 |
| abstract_inverted_index.(’m-ar-K’) | 91 |
| abstract_inverted_index.classification | 46, 162, 202 |
| abstract_inverted_index.dimensionality | 6, 216 |
| abstract_inverted_index.distributions, | 270 |
| abstract_inverted_index.m-ar-K-FastICA | 215, 261 |
| abstract_inverted_index.(’FastICA’) | 101 |
| abstract_inverted_index.non-Gaussianity | 147, 242 |
| abstract_inverted_index.pseudo-Gaussian | 258 |
| abstract_inverted_index.standardisation | 157 |
| abstract_inverted_index.’m-ar-K-PCA’ | 108 |
| abstract_inverted_index.’scikit-learn’, | 125 |
| abstract_inverted_index.’m-ar-K-FastICA’ | 110 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.05875935 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |