Feature selection using principal feature analysis

By Yijuan Lu, Ira Cohen, Xiang Sean Zhou, Qi Tian. ACM Multimedia, 2007

384 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/7c2fc21a4d0c7db7748100d6690f039135788c5a

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Xuelong Li
2. Dacheng Tao
3. Feiping Nie
4. Nojun Kwak
5. Wei Peng
6. Ling Shao
7. Yi Yang
8. Seonguk Park

In Media Technology

1. Xuelong Li
2. glenn. fung
3. Feiping Nie
4. Wei Peng
5. Honglak Lee
6. Emily Mower Provost
7. Yelin Kim
8. A. Ghodsi

Citations in the corpus

• Deep learning for robust feature generation in audiovisual emotion recognition (2013, methodology): “The first uses IG [36] and the second uses PFA [21] feature selection (a supervised and unsupervised feature selection technique, respectively).”
• Survey on SDN based network intrusion detection system using machine learning approaches (2018, methodology): “In recent years, there has been active research works on feature selection, where they use various feature selection methods such as principal feature analysis, Bi-Layer behavioural-based feature selection approach, and Random Forest to reduce feature [50 – 52].”
• Advances in variable selection methods I: Causal selection methods versus stepwise regression and principal component analysis on data of known and unknown functional relationships (2012, methodology): “The principal component analysis method (PCA) implemented in this study is based on recommendations of Lu et al. (2007).”
• Convex Principal Feature Selection (2010, methodology): “In particular, we compare CPFS to SPCA, two eigenvector-loading-based methods by Jolliffe [6], iterative (I) and non-iterative (NI), and Principal feature analysis (PFA) [9].”
• Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2014, background): “Besides, there are also some other related researches to extend PCA for feature selection, such as Principle Feature Analysis [31] and the works in [32], [33].”
• An Efficient Greedy Method for Unsupervised Feature Selection (2011, methodology): “Lu et al. [5] suggests a different PCA-based approach which applies k-means clustering to the principal components, and then selects the features that are close to clusters' centroids.”