Principal Component Analysis and Factor Analysis

By Ian T. Jolliffe. 1986

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sabine Süsstrunk
2. Edo Collins
3. G. Wang
4. Wenbo Guo
5. Radhakrishna Achanta
6. Xinyu Xing
7. Nina Sumiko Tomita Hirata
8. Mateus Espadoto

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Representation Disentanglement [113, 279, 335, 336, 337, 338, 339, 340, 341, 342]”
• Deep Feature Factorization For Concept Discovery (2018, methodology): “A classical method for dimensionality reduction is principal component analysis (PCA) [18].”
• Deep learning multidimensional projections (2019, methodology): “llows. For more extensive reviews of DR methods, and their quality features, we refer to [HG02,vP09,EHH12,SVPM14, LMW 15,CG15,XLX17]. Probably the best known DR method is Principal Component Analysis [Jol86] (PCA), which has been used in several areas for many decades. It is a very simple algorithm ”
• LEMNA: Explaining Deep Learning based Security Applications (2018, methodology): “As a side note, we want to clarify that machine learning explanation is completely different from feature selection methods such as Principal Component Analysis (PCA) [26], Sparse Coding [39] or Chi-square Statistics [49].”