Feature Extraction Methods Based on Traditional Statistics and Deep Learning

Юсупов, О.Р., Хандамов, Й.Х., Хожиакбаров, Ш.М.

Рақамли технологияларнинг назарий ва амалий масалалари · 2025-yil

Annotatsiya

The article presents a theoretical and practical comparative analysis of four important methods related to feature extraction and dimensionality reduction: Fisher Discriminant Analysis (FDA), Canonical Correlation Analysis (CCA), Deep Canonical Correlation Analysis (DCCA), and Deep Multiset Canonical Correlation Analysis (DMCCA). The theoretical foundations, advantages, and disadvantages of each method are discussed in detail. In addition, their applicability across various domains is highlighted. The analyses show that FDA is effective in supervised learning, while CCA serves as an important tool for studying relationships between two sources. DCCA extends classical approaches through deep learning, enabling the identification of nonlinear structures. DMCCA, in turn, constructs a common latent representation for multi-source data and demonstrates high efficiency in modern artificial intelligence systems.

Maqola ma’lumotlari
MualliflarЮсупов, О.Р., Хандамов, Й.Х., Хожиакбаров, Ш.М.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2025-09-25
Jild8
Son3
Betlar101-109
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v8i3.293
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v8i3.293 · Maqolaning asl sahifasi

Kalit so‘zlar

извлечение признаков, сокращение размерности, FDA, CCA, DCCA, DMCCA, латентное представление, статистические методы, глубокое обучение, мультимодальные данные, feature extraction, dimensionality reduction, FDA, CCA, DCCA, DMCCA, latent representation, statistical methods, deep learning, multimodal data

Ilmiy soha

Texnika

Iqtibos keltirish

APAЮсупов, О.Р., Хандамов, Й.Х., & Хожиакбаров, Ш.М. (2025). Feature Extraction Methods Based on Traditional Statistics and Deep Learning. Рақамли технологияларнинг назарий ва амалий масалалари, 8(3), 101-109. https://doi.org/10.62132/ijdt.v8i3.293
GOST (OAK)Юсупов, О.Р., Хандамов, Й.Х., Хожиакбаров, Ш.М. Feature Extraction Methods Based on Traditional Statistics and Deep Learning // Рақамли технологияларнинг назарий ва амалий масалалари. – 2025. – T. 8, № 3. – P. 101-109. – DOI: 10.62132/ijdt.v8i3.293.
MLAЮсупов, О.Р., Хандамов, Й.Х., and Хожиакбаров, Ш.М. “Feature Extraction Methods Based on Traditional Statistics and Deep Learning.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(3), 101-109, 2025. https://doi.org/10.62132/ijdt.v8i3.293
ChicagoЮсупов, О.Р., Хандамов, Й.Х., and Хожиакбаров, Ш.М. “Feature Extraction Methods Based on Traditional Statistics and Deep Learning.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(3), 101-109 (2025). https://doi.org/10.62132/ijdt.v8i3.293
HarvardЮсупов, О.Р., Хандамов, Й.Х., and Хожиакбаров, Ш.М (2025) ‘Feature Extraction Methods Based on Traditional Statistics and Deep Learning’, Рақамли технологияларнинг назарий ва амалий масалалари, 8(3), 101-109. https://doi.org/10.62132/ijdt.v8i3.293
BibTeX
@article{article2025,
  title = {Feature Extraction Methods Based on Traditional Statistics and Deep Learning},
  author = {Юсупов, О.Р. and Хандамов, Й.Х. and Хожиакбаров, Ш.М.},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2025},
  volume = {8},
  number = {3},
  pages = {101-109},
  doi = {10.62132/ijdt.v8i3.293},
  url = {https://doi.org/10.62132/ijdt.v8i3.293}
}

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