|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM344588912 |
| 003 |
DE-627 |
| 005 |
20250303163202.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2022 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2022.3195669
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1148.xml
|
| 035 |
|
|
|a (DE-627)NLM344588912
|
| 035 |
|
|
|a (NLM)35939456
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhang, Rui
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Graph Convolution RPCA With Adaptive Graph
|
| 264 |
|
1 |
|c 2022
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 23.09.2022
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Principal component analysis (PCA) is warmly welcomed in dimensionality reduction and its applications. Due to the high sensitivity of PCA to outliers, a series of PCA methods are proposed to enhance the robustness of PCA. Besides, the representation ability of the existing PCA methods has limitations as well. To enhance the robustness and representation ability of robust PCA, we elaborate a novel Graph Convolution Robust PCA method (GRPCA) to incorporate the manifold structure into PCA. It constructs a sparse graph based on the local connectivity structure of samples. Graph auto-encoder is utilized to solve the robust PCA problem under the low-rank and sparse constraints. With the dual-decoder, GRPCA learns the low-dimensional embeddings that reconstruct the manifold structure and low-rank approximation simultaneously. Furthermore, since the graph suffers from misconnection triggered by occlusions, the local connectivity structure of low-dimensional embeddings is utilized to modify the graph. Our proposed method excels in both the clustering of low-dimensional embeddings and the low-rank recovery. Lastly, extensive experiments conducted on six real-world datasets demonstrated the efficiency and superiority of the proposed GRPCA
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Zhang, Wenlin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Li, Pei
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Li, Xuelong
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 08., Seite 6062-6071
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
|
| 773 |
1 |
8 |
|g volume:31
|g year:2022
|g day:08
|g pages:6062-6071
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2022.3195669
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 31
|j 2022
|b 08
|h 6062-6071
|