Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors

2013 ◽  
Vol 35 (5) ◽  
pp. 1164-1177 ◽  
Author(s):  
Chi Ho Chan ◽  
Muhammad Atif Tahir ◽  
Josef Kittler ◽  
Matti Pietikainen
Keyword(s):  
Face Recognition ◽  
Local Phase ◽  
Local Phase Quantization ◽  
Kernel Fusion ◽  
Phase Quantization

scholarly journals Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zhu-Hong You ◽  
Yu-Hong Fang ◽  
Yu-Jun Zhao ◽  
...  
Keyword(s):  
Protein Sequences ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Computational Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Local Phase ◽  
Local Phase Quantization ◽  
Phase Quantization ◽  
Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

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