A Novel Non-iterative Training Method for CNN Classifiers Using Gram–Schmidt Process Article Swipe
Basim Azam
,
Deepthi Kuttichira
,
Pubudu Sanjeewani
,
Brijesh Verma
,
Ashfaqur Rahman
,
Lipo Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-025-11741-1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-025-11741-1
Convolutional neural networks have become prominent machine learning models, particularly in the realm of computer vision, due to their ability to predict and extract robust features from raw image data. CNNs, similar to other neural network models, undergo training via backpropagation, an iterative technique. However, the backpropagation algorithm has notable challenges, including slow convergence, susceptibility to local minima, and hypersensitivity to learning rates. These challenges not only impact the model’s accuracy but also make the training process computationally intensive. To address these limitations, We introduce a novel approach that trains the CNN classifier using a non-iterative learning method. The proposed approach involves automatic extraction of pertinent features from the raw-data, followed by the application of Gram–Schmidt process to decompose the feature matrix and determine classifier’s weights. The proposed method has shown enhanced predictive accuracy over state-of-the-art models when evaluated on two benchmark datasets, MNIST and CIFAR-10. The extensive experimentation using most cited pre-trained experiments validate the effectiveness of our proposed method.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11063-025-11741-1
- https://link.springer.com/content/pdf/10.1007/s11063-025-11741-1.pdf
- OA Status
- hybrid
- Cited By
- 2
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408181158
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https://openalex.org/W4408181158Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11063-025-11741-1Digital Object Identifier
- Title
-
A Novel Non-iterative Training Method for CNN Classifiers Using Gram–Schmidt ProcessWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-06Full publication date if available
- Authors
-
Basim Azam, Deepthi Kuttichira, Pubudu Sanjeewani, Brijesh Verma, Ashfaqur Rahman, Lipo WangList of authors in order
- Landing page
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https://doi.org/10.1007/s11063-025-11741-1Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s11063-025-11741-1.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11063-025-11741-1.pdfDirect OA link when available
- Concepts
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Computational intelligence, Artificial intelligence, Computer science, Gram, Pattern recognition (psychology), Iterative and incremental development, Process (computing), Training (meteorology), Mathematics, Machine learning, Physics, Operating system, Biology, Meteorology, Genetics, Software engineering, BacteriaTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.MNIST | 144 |
| abstract_inverted_index.These | 64 |
| abstract_inverted_index.cited | 152 |
| abstract_inverted_index.data. | 30 |
| abstract_inverted_index.image | 29 |
| abstract_inverted_index.local | 57 |
| abstract_inverted_index.novel | 87 |
| abstract_inverted_index.other | 34 |
| abstract_inverted_index.realm | 13 |
| abstract_inverted_index.shown | 131 |
| abstract_inverted_index.their | 19 |
| abstract_inverted_index.these | 82 |
| abstract_inverted_index.using | 94, 150 |
| abstract_inverted_index.become | 5 |
| abstract_inverted_index.impact | 68 |
| abstract_inverted_index.matrix | 122 |
| abstract_inverted_index.method | 129 |
| abstract_inverted_index.models | 137 |
| abstract_inverted_index.neural | 2, 35 |
| abstract_inverted_index.rates. | 63 |
| abstract_inverted_index.robust | 25 |
| abstract_inverted_index.trains | 90 |
| abstract_inverted_index.ability | 20 |
| abstract_inverted_index.address | 81 |
| abstract_inverted_index.extract | 24 |
| abstract_inverted_index.feature | 121 |
| abstract_inverted_index.machine | 7 |
| abstract_inverted_index.method. | 98, 161 |
| abstract_inverted_index.minima, | 58 |
| abstract_inverted_index.models, | 9, 37 |
| abstract_inverted_index.network | 36 |
| abstract_inverted_index.notable | 50 |
| abstract_inverted_index.predict | 22 |
| abstract_inverted_index.process | 77, 117 |
| abstract_inverted_index.similar | 32 |
| abstract_inverted_index.undergo | 38 |
| abstract_inverted_index.vision, | 16 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 45 |
| abstract_inverted_index.accuracy | 71, 134 |
| abstract_inverted_index.approach | 88, 101 |
| abstract_inverted_index.computer | 15 |
| abstract_inverted_index.enhanced | 132 |
| abstract_inverted_index.features | 26, 107 |
| abstract_inverted_index.followed | 111 |
| abstract_inverted_index.involves | 102 |
| abstract_inverted_index.learning | 8, 62, 97 |
| abstract_inverted_index.networks | 3 |
| abstract_inverted_index.proposed | 100, 128, 160 |
| abstract_inverted_index.training | 39, 76 |
| abstract_inverted_index.validate | 155 |
| abstract_inverted_index.weights. | 126 |
| abstract_inverted_index.CIFAR-10. | 146 |
| abstract_inverted_index.algorithm | 48 |
| abstract_inverted_index.automatic | 103 |
| abstract_inverted_index.benchmark | 142 |
| abstract_inverted_index.datasets, | 143 |
| abstract_inverted_index.decompose | 119 |
| abstract_inverted_index.determine | 124 |
| abstract_inverted_index.evaluated | 139 |
| abstract_inverted_index.extensive | 148 |
| abstract_inverted_index.including | 52 |
| abstract_inverted_index.introduce | 85 |
| abstract_inverted_index.iterative | 43 |
| abstract_inverted_index.model’s | 70 |
| abstract_inverted_index.pertinent | 106 |
| abstract_inverted_index.prominent | 6 |
| abstract_inverted_index.raw-data, | 110 |
| abstract_inverted_index.challenges | 65 |
| abstract_inverted_index.classifier | 93 |
| abstract_inverted_index.extraction | 104 |
| abstract_inverted_index.intensive. | 79 |
| abstract_inverted_index.predictive | 133 |
| abstract_inverted_index.technique. | 44 |
| abstract_inverted_index.application | 114 |
| abstract_inverted_index.challenges, | 51 |
| abstract_inverted_index.experiments | 154 |
| abstract_inverted_index.pre-trained | 153 |
| abstract_inverted_index.convergence, | 54 |
| abstract_inverted_index.limitations, | 83 |
| abstract_inverted_index.particularly | 10 |
| abstract_inverted_index.Convolutional | 1 |
| abstract_inverted_index.effectiveness | 157 |
| abstract_inverted_index.non-iterative | 96 |
| abstract_inverted_index.Gram–Schmidt | 116 |
| abstract_inverted_index.classifier’s | 125 |
| abstract_inverted_index.susceptibility | 55 |
| abstract_inverted_index.backpropagation | 47 |
| abstract_inverted_index.computationally | 78 |
| abstract_inverted_index.experimentation | 149 |
| abstract_inverted_index.backpropagation, | 41 |
| abstract_inverted_index.hypersensitivity | 60 |
| abstract_inverted_index.state-of-the-art | 136 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.96867757 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |