Name
Abstract | ||
|---|---|---|
Principal Component Analysis (PCA) is a common tool for dimensionality reduction and feature extraction, which has been applied in many fields, such as biology, medicine, machine learning and bioinformatics. But PCA has two obvious drawbacks: each principal component is line combination and loadings are non-zero which is hard to interpret. Sparse Principal Component Analysis (SPCA) was proposed to overcome these two disadvantages of PCA under the circumstances. This review paper will mainly focus on the research about SPCA, where the basic models of PCA and SPCA, various algorithms and extensions of SPCA are summarized. According to the difference of objective function and the constraint conditions, SPCA can be divided into three groups as it shown in Fig. 1. We also make a comparison among the different kind of sparse penalties. Besides, brief statements and other different classifications are summarized at last. © Springer International Publishing Switzerland 2016. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-42294-7_33 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
Principal component analysis, Sparse principal component analysis, Rotation, Sparse constraint | Sparse PCA,Dimensionality reduction,Pattern recognition,Computer science,Sparse approximation,Feature extraction,Artificial intelligence,Principal component analysis | Conference |
Volume | ISSN | Citations |
9772 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 26 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Chun-Mei | 1 | 4 | 2.42 |
| Gao Ying-Lian | 2 | 29 | 18.73 |
| Liu Jin-Xing | 3 | 40 | 16.11 |
| Chun-hou Zheng | 4 | 732 | 71.79 |
| Li Shengjun | 5 | 4 | 3.13 |
| Dong Wang | 6 | 2 | 1.38 |