Application of Approximate Equality for Reduction of Feature Vector Dimension

2016 ◽  
Vol 11 (1) ◽  
pp. 26-40
Author(s):  
Kumar Ray ◽  
Srikanta Kolay
Keyword(s):  
Feature Vector ◽  
Approximate Equality ◽  
Vector Dimension

Study of Face Feature Extraction and Recognition Techniques Based on Wavelet Analysis

2014 ◽  
Vol 1030-1032 ◽  
pp. 1810-1813
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Face Recognition ◽  
Wavelet Decomposition ◽  
Feature Vector ◽  
Recognition Performance ◽  
Image Database ◽  
Face Image ◽  
Support Vector ◽  
Svm Model ◽  
Feature Based ◽  
Vector Dimension

Face recognition is a research hotspot of pattern recognition and artificial intelligence. This paper presents a method of extract face feature based on Wavelet. First, reduce vector dimension by wavelet decomposition of the image, second, train the multi class support vector machine (SVM) model by face feature vector extracted and make face recognition finally. The experiments on ORL face image database of the algorithm shows the superiority of the proposed algorithm in terms of recognition performance.

Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks

2018 ◽  
Vol 16 (05) ◽  
pp. 1850021 ◽  
Author(s):  
Yanbu Guo ◽  
Bingyi Wang ◽  
Weihua Li ◽  
Bei Yang
Keyword(s):  
Neural Networks ◽  
Amino Acid ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Feature Vector ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Two Dimensional ◽  
The Matrix ◽  
Vector Dimension

Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature vector dimension. Common approaches to predict secondary structures only focus on the amino-acid residue dimension. However, the feature vector dimension may also contain useful information for PSSP. To integrate the information on both dimensions of the matrix, we propose a hybrid deep learning framework, two-dimensional convolutional bidirectional recurrent neural network (2C-BRNN), for improving the accuracy of 8-class secondary structure prediction. The proposed hybrid framework is to extract the discriminative local interactions between amino-acid residues by two-dimensional convolutional neural networks (2DCNNs), and then further capture long-range interactions between amino-acid residues by bidirectional gated recurrent units (BGRUs) or bidirectional long short-term memory (BLSTM). Specifically, our proposed 2C-BRNNs framework consists of four models: 2DConv-BGRUs, 2DCNN-BGRUs, 2DConv-BLSTM and 2DCNN-BLSTM. Among these four models, the 2DConv- models only contain two-dimensional (2D) convolution operations. Moreover, the 2DCNN- models contain 2D convolutional and pooling operations. Experiments are conducted on four public datasets. The experimental results show that our proposed 2DConv-BLSTM model performs significantly better than the benchmark models. Furthermore, the experiments also demonstrate that the proposed models can extract more meaningful features from the matrix of proteins, and the feature vector dimension is also useful for PSSP. The codes and datasets of our proposed methods are available at https://github.com/guoyanb/JBCB2018/ .

Feature Selection Based on Evolution Strategy for Character Recognition

2018 ◽  
Vol 18 (03) ◽  
pp. 1850014 ◽  
Author(s):  
Soukaina Benchaou ◽  
M'Barek Nasri ◽  
Ouafae El Melhaoui
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Feature Vector ◽  
Recognition Rate ◽  
Evolution Strategy ◽  
Selection System ◽  
Training Time ◽  
Proposed Model ◽  
Interesting Area ◽  
Vector Dimension

Handwriting, printed character recognition is an interesting area in image processing and pattern recognition. It consists of a number of phases which are preprocessing, feature extraction and classification. The phase of feature extraction is carried out by different techniques; zoning, profile projection, and ameliored Freeman. The high number of features vector can increase the error rate and the training time. So, to solve this problem, we present in this paper a new method of selecting attributes based on the evolution strategy in order to reduce the feature vector dimension and to improve the recognition rate. The proposed model has been applied to recognize numerals and it obtained a better results and showed more robustness than without the selection system.

Deep Convolution Feature Vector for Fast Face Image Retrieval

2018 ◽  
Vol 30 (12) ◽  
pp. 2311
Author(s):  
Zhendong Li ◽  
Yong Zhong ◽  
Dongping Cao
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Face Image

Convolutional Neural Networks for ATC Classification

2019 ◽  
Vol 24 (34) ◽  
pp. 4007-4012 ◽  
Author(s):  
Alessandra Lumini ◽  
Loris Nanni
Keyword(s):  
Classification System ◽  
Feature Vector ◽  
Chemical Interaction ◽  
Anatomical Therapeutic Chemical ◽  
World Health ◽  
Therapeutic Effects ◽  
True Rate ◽  
Unknown Compound ◽  
The Absolute ◽  
Learned Features

Background: Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound’s possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult. Methods: In this paper, we propose a multi-label classifier system based on deep learned features to infer the ATC classification. The system is based on a 2D representation of the samples: first a 1D feature vector is obtained extracting information about a compound’s chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes, then the original 1D feature vector is reshaped to obtain a 2D matrix representation of the compound. Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule. Results: Experimental evaluation based on rigorous cross-validation demonstrates the superior prediction quality of this method compared to other state-of-the-art approaches developed for this problem. Conclusion: Extensive experiments demonstrate that the new predictor, based on CNN, outperforms other existing predictors in the literature in almost all the five metrics used to examine the performance for multi-label systems, particularly in the “absolute true” rate and the “absolute false” rate, the two most significant indexes. Matlab code will be available at https://github.com/LorisNanni.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

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