পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Canonical Correlation Analysis× | ডিস্ক্রিমিন্যান্ট অ্যানালাইসিস (Discriminant Analysis)× | |
|---|---|---|
| ক্ষেত্র | পরিসংখ্যান | পরিসংখ্যান |
| পরিবার | Latent structure | Latent structure |
| উদ্ভবের বছর | 1936 | 1936 |
| প্রবর্তক≠ | Harold Hotelling | Ronald A. Fisher |
| ধরন≠ | Multivariate linear dimension reduction and association | Supervised classification and dimension reduction |
| মৌলিক উৎস≠ | Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377. DOI ↗ | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| অপর নাম | CCA, canonical variate analysis, canonical analysis, multiple canonical correlation | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| সম্পর্কিত | 4 | 4 |
| সারসংক্ষেপ≠ | Canonical Correlation Analysis (CCA) is a multivariate statistical method that identifies pairs of linear combinations — one from each of two variable sets — such that the correlation between each pair is maximised. Introduced by Harold Hotelling in his landmark 1936 Biometrika paper, CCA provides the most general linear framework for studying the association between two multivariate batteries of measurements, and many classical procedures (multiple regression, MANOVA, discriminant analysis) are special cases of it. | Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error. |
| ScholarGateডেটাসেট ↗ |
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