Simple principal components

By S. K. Vines. 2000

147 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/777ae5fc47aedc7519295192cc86c55627b33873

How researchers see this paper

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

In Analytical Chemistry

1. Robert Tibshirani
2. Trevor J. Hastie
3. Jianhua Z. Huang
4. Haipeng Shen
5. Nickolay T. Trendafilov
6. Ian T. Jolliffe
7. Hui Zou
8. Mudassir Uddin

In Applied Mathematics

1. Mudassir Uddin
2. Nickolay T. Trendafilov
3. Ian T. Jolliffe
4. Robert Tibshirani
5. Trevor J. Hastie
6. Jianhua Z. Huang
7. Haipeng Shen
8. Hui Zou

In Statistics and Probability

1. Robert Tibshirani
2. Trevor J. Hastie
3. Jianqing Fan
4. Hui Zou
5. Peter Buhlmann
6. Francis R. Bach
7. Nicolai Meinshausen
8. Cun-Hui Zhang

Citations in the corpus

• Full regularization path for sparse principal component analysis (2007, unknown): “Moreover, in some applications, nonzero coe.cients have a direct cost (e.g., transaction costs in .nance) hence there may be a direct trade-o. between statistical .delity and practicality.”
• Sparse Principal Component Analysis (2006, background): “(Jollifie 1995). Vines (2000) considered simple principal components by restricting the loadings to”
• Human resources development practices and their association with employee attitudes: Between traditional and new careers (2007, unknown): “Scree-plots for the two sets of data indicated a single dimension, which was further confirmed by a simple component analysis (Vines, 2000) that led to equal loadings (1) for all items in both samples.”
• A Direct Formulation for Sparse PCA Using Semidefinite Programming (2004, unknown): “[2] considered simple principal components by restricting the loadings to take values from a small set of allowable integers, such as 0, 1, and−1.”
• Sparse principal component analysis via regularized low rank matrix approximation (2008, background): “Vines (2000) considered simple components , whose loadings are restricted to only integers such as 0, 1 and -1.”
• Principal Component Analysis on Spatial Data: An Overview (2013, unknown): “Calvo (1998) and Vines (2000).”
• A Modified Principal Component Technique Based on the LASSO (2003, methodology): “As with the methods of Hausman (1982) and Vines (2000), and unlike rotation, our technique usually produces some exactly zero loadings in the components.”