পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ফ্যাক্টর অ্যানালাইসিস (Factor Analysis)× | প্রধান উপাদান বিশ্লেষণ× | |
|---|---|---|
| ক্ষেত্র≠ | গবেষণা পরিসংখ্যান | যন্ত্র শিখন |
| পরিবার≠ | Process / pipeline | Machine learning |
| উদ্ভবের বছর≠ | 1931 | 2002 |
| প্রবর্তক≠ | Louis Leon Thurstone | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| ধরন≠ | Method | Unsupervised dimensionality reduction |
| মৌলিক উৎস≠ | Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| অপর নাম≠ | EFA, CFA, latent variable modeling | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| সম্পর্কিত | 3 | 3 |
| সারসংক্ষেপ≠ | Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateডেটাসেট ↗ |
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