Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds

By Elnaz Barshan, A. Ghodsi, Zohreh Azimifar, Mansoor Zolghadri Jahromi. Pattern Recognition, 2011

358 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e26103bb90219403987ca4d36e26d7f4d31ce6b0

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. Chunhui Zhao
2. Liangjun Feng

In Computer Vision and Pattern Recognition

1. Chunhui Zhao
2. Liangjun Feng

In Molecular Biology

1. Chunhui Zhao
2. Liangjun Feng

Citations in the corpus

• Linear discriminant analysis: A detailed tutorial (2017, neutral): “This type of data reduction is suitable for many applications such as visualization [2,40], and noise removal [70].”
• Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis (2021, methodology): “However, for the fault diagnosis task [33], which is generally defined in the supervised learning paradigm, the supervised principle component analysis [34] can be more helpful for the extraction of target related features and presenting an effective diagnosis.”