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.
| Mualliflar | Юсупов, О.Р., Хандамов, Й.Х., Хожиакбаров, Ш.М. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2025-09-25 |
| Jild | 8 |
| Son | 3 |
| Betlar | 101-109 |
| Til | Rus |
| DOI | 10.62132/ijdt.v8i3.293 |
DOI: 10.62132/ijdt.v8i3.293 · Maqolaning asl sahifasi
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
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