An efficient classification method based on principal component and sparse representation.

Lin Zhai, Shujun Fu, Caiming Zhang, Yunxian Liu, Lu Wang, Guohua Liu, Mingqiang Yang

Abstract read

In one paragraph

Article in SpringerPlus, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Lin ZhaiSchool of Mathematics, Shandong University, Shanda Nanlu 27, Jinan, 250100 China.

Shujun FuSchool of Mathematics, Shandong University, Shanda Nanlu 27, Jinan, 250100 China.

Caiming ZhangSchool of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250061 China ; School of Computer Science and Technology, Shandong University, Jinan, 250101 China.

Yunxian LiuSchool of Mathematics, Shandong University, Shanda Nanlu 27, Jinan, 250100 China.

Lu WangSchool of Public Health, Shandong University, Jinan, 250012 China.

Guohua LiuDepartment of Ophthalmology, Qilu Children's Hospital of Shandong University, Jinan, 250022 China.

Mingqiang YangSchool of Information Science and Engineering, Shandong University, Jinan, 250100 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

Indexed as

Image classificationPalmprint recognitionPrincipal component analysisSparse representationSubspace optimization

Identifiers

PMID27386281

DOI10.1186/s40064-016-2511-z

PMCPMC4917515

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.