Ieee Southeastcon 2014 2014
DOI: 10.1109/secon.2014.6950744
|View full text |Cite
|
Sign up to set email alerts
|

Evaluation of normalization and PCA on the performance of classifiers for protein crystallization images

Abstract: In this paper, we investigate the performance of classification of protein crystallization images captured during protein crystal growth process. We group protein crystallization images into 3 categories: noncrystals, likely leads (conditions that may yield formation of crystals) and crystals. In this research, we only consider the subcategories of noncrystal and likely leads protein crystallization images separately. We use 5 different classifiers to solve this problem and we applied some data preprocessing m… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
9
0

Year Published

2016
2016
2024
2024

Publication Types

Select...
7
1
1
1

Relationship

0
10

Authors

Journals

citations
Cited by 15 publications
(9 citation statements)
references
References 7 publications
0
9
0
Order By: Relevance
“…The Bartlett's test of sphericity was carried out and the Kaiser-Meyer-Olkin (KMO) index was calculated from the data matrix [39,40]. The data matrix was subsequently centred and scaled columnwise and the corresponding cell values were, thus, transformed into Z-scores [41]. Based on the outcomes of the Bartlett's test of sphericity and of the KMO index, a principal component analysis (PCA) was performed on the transformed data matrix.…”
Section: Discussionmentioning
confidence: 99%
“…The Bartlett's test of sphericity was carried out and the Kaiser-Meyer-Olkin (KMO) index was calculated from the data matrix [39,40]. The data matrix was subsequently centred and scaled columnwise and the corresponding cell values were, thus, transformed into Z-scores [41]. Based on the outcomes of the Bartlett's test of sphericity and of the KMO index, a principal component analysis (PCA) was performed on the transformed data matrix.…”
Section: Discussionmentioning
confidence: 99%
“…In this paper, the normalized feature values lie within [−1, 1]. It helps in improving the recognition accuracy as it accelerates the training step [29,30]. For normalized feature vector, SVM with PUK shows best recognition accuracy of 90%.…”
Section: Resultsmentioning
confidence: 99%
“…The Bartlett's test of sphericity was carried out, and the Kaiser-Meyer-Olkin (KMO) index was calculated from the data matrix [26,27]. The two data matrices were centred and column-wise scaled, and then the relative cell values were transformed into Z-scores [28]. Varimax rotation of the principal axes was applied to the data matrices.…”
Section: Discussionmentioning
confidence: 99%