scholarly journals Adaptive Multilevel Kernel Machine for Scene Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Junlin Hu ◽  
Liang Wang ◽  
Fuqing Duan ◽  
Ping Guo
Keyword(s):  
Principal Component ◽  
Support Vector ◽  
Scene Classification ◽  
Machine Method ◽  
Census Transform ◽  
Kernel Machine ◽  
Benchmark Datasets ◽  
The Difference ◽  
Optimal Kernel ◽  
Special Complex

Scene classification is a challenging problem in computer vision applications and can be used to model and analyze a special complex system, the internet community. The spatial PACT (Principal component Analysis of Census Transform histograms) is a promising representation for recognizing instances and categories of scenes. However, since the original spatial PACT only simply concatenates compact census transform histograms at all levels together, all levels have the same contribution, which ignores the difference among various levels. In order to ameliorate this point, we propose an adaptive multilevel kernel machine method for scene classification. Firstly, it computes a set of basic kernels at each level. Secondly, an effective adaptive weight learning scheme is employed to find the optimal weights for best fusing all these base kernels. Finally, support vector machine with the optimal kernel is used for scene classification. Experiments on two popular benchmark datasets demonstrate that the proposed adaptive multilevel kernel machine method outperforms the original spatial PACT. Moreover, the proposed method is simple and easy to implement.

scholarly journals Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3983
Author(s):  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Kristina Serec ◽  
Matej Par ◽  
Marija Baković ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Principal Component ◽  
Support Vector ◽  
Gender Recognition ◽  
Proof Of Concept ◽  
Male And Female ◽  
Tooth Type ◽  
Tooth Apex ◽  
The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

scholarly journals Using discriminative vector machine model with 2DPCA to predict interactions among proteins

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Zhengwei Li ◽  
Ru Nie ◽  
Zhuhong You ◽  
Chen Cao ◽  
Jiashu Li
Keyword(s):  
Protein Interactions ◽  
False Positive Rate ◽  
Principal Component ◽  
Amino Acid Sequences ◽  
Support Vector ◽  
Machine Model ◽  
Discriminative Feature ◽  
Benchmark Datasets ◽  
Low Efficiency ◽  
H Pylori

Abstract Background The interactions among proteins act as crucial roles in most cellular processes. Despite enormous effort put for identifying protein-protein interactions (PPIs) from a large number of organisms, existing firsthand biological experimental methods are high cost, low efficiency, and high false-positive rate. The application of in silico methods opens new doors for predicting interactions among proteins, and has been attracted a great deal of attention in the last decades. Results Here we present a novelty computational model with the adoption of our proposed Discriminative Vector Machine (DVM) model and a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to identify candidate PPIs only based on protein sequences. To be more specific, a 2DPCA descriptor is employed to capture discriminative feature information from Position-Specific Scoring Matrix (PSSM) of amino acid sequences by the tool of PSI-BLAST. Then, a robust and powerful DVM classifier is employed to infer PPIs. When applied on both gold benchmark datasets of Yeast and H. pylori, our model obtained mean prediction accuracies as high as of 97.06 and 92.89%, respectively, which demonstrates a noticeable improvement than some state-of-the-art methods. Moreover, we constructed Support Vector Machines (SVM) based predictive model and made comparison it with our model on Human benchmark dataset. In addition, to further demonstrate the predictive reliability of our proposed method, we also carried out extensive experiments for identifying cross-species PPIs on five other species datasets. Conclusions All the experimental results indicate that our method is very effective for identifying potential PPIs and could serve as a practical approach to aid bioexperiment in proteomics research.

Out-of-sample data visualization using bi-kernel t-SNE

2020 ◽  
pp. 147387162097820
Author(s):  
Haili Zhang ◽  
Pu Wang ◽  
Xuejin Gao ◽  
Yongsheng Qi ◽  
Huihui Gao
Keyword(s):  
Data Visualization ◽  
Principal Component ◽  
Feature Space ◽  
Gaussian Kernel ◽  
Sample Data ◽  
Out Of Sample ◽  
Benchmark Datasets ◽  
The Difference ◽  
Low Dimensional ◽  
Effective Visualization

T-distributed stochastic neighbor embedding (t-SNE) is an effective visualization method. However, it is non-parametric and cannot be applied to steaming data or online scenarios. Although kernel t-SNE provides an explicit projection from a high-dimensional data space to a low-dimensional feature space, some outliers are not well projected. In this paper, bi-kernel t-SNE is proposed for out-of-sample data visualization. Gaussian kernel matrices of the input and feature spaces are used to approximate the explicit projection. Then principal component analysis is applied to reduce the dimensionality of the feature kernel matrix. Thus, the difference between inliers and outliers is revealed. And any new sample can be well mapped. The performance of the proposed method for out-of-sample projection is tested on several benchmark datasets by comparing it with other state-of-the-art algorithms.

Detecting protagonists and antagonists in the voice quality of American cartoon characters: a quantitative LTAS-based analysis

Phonetica ◽  
2021 ◽  
Vol 78 (4) ◽  
pp. 345-384
Author(s):  
Ke Hui Tong ◽  
Scott Reid Moisik
Keyword(s):  
A Priori ◽  
Voice Quality ◽  
Principal Component ◽  
Acoustic Properties ◽  
Support Vector ◽  
Cartoon Character ◽  
Quantitative Examination ◽  
The Difference ◽  
Cartoon Characters ◽  
Dominance Pattern

Abstract The voices of heroes and villains in cartoons contribute to their uniqueness and helps shape how we perceive them. However, not much research has looked at the acoustic properties of character voices and the possible contributions these have to cartoon character archetypes. We present a quantitative examination of how voice quality distinguishes between characters based on their alignment as either protagonists or antagonists, performing Principal Component Analysis (PCA) on the Long-term Average Spectra (LTAS) of concatenated passages of the speech of various characters obtained from four different animated cartoons. We then assessed if the categories of “protagonists” and “antagonists” (determined via an a priori classification) could be distinguished using a classification algorithm, and if so, what acoustic characteristics could help distinguish the two categories. The overall results support the idea that protagonists and antagonists can be distinguished by their voice qualities. Support Vector Machine (SVM) analysis yielded an average classification accuracy of 96% across the cartoons. Visualisation of the spectral traits constituting the difference did not yield consistent results but reveals a low-versus-high frequency energy dominance pattern segregating antagonists and protagonists. Future studies can look into how other variables might be confounded with voice quality in distinguishing between these categories.

scholarly journals A Detailed Analysis on Kidney and Heart Disease Prediction using Machine Learning

2021 ◽  
pp. 9-14
Author(s):  
Claire Salkar
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Heart Disease ◽  
Kidney Disease ◽  
Principal Component ◽  
Support Vector ◽  
Computationally Efficient ◽  
Optimal Method ◽  
The Difference

Detection of disease at earlier stages is the most challenging one. Datasets of different diseases are available online with different number of features corresponding to a particular disease. Many dimensionalities reduction and feature extraction techniques are used nowadays to reduce the number of features in dataset and finding the most appropriate ones. This paper explores the difference in performance of different machine learning models using Principal Component Analysis dimensionality reduction technique on the datasets of Chronic kidney disease and Cardiovascular disease. Further, the authors apply Logistic Regression, K Nearest Neighbour, Naïve Bayes, Support Vector Machine and Random Forest Model on the datasets and compare the performance of the model with and without PCA. A key challenge in the field of data mining and machine learning is building accurate and computationally efficient classifiers for medical applications. With an accuracy of 100% in chronic kidney disease and 85% for heart disease, KNN classifier and logistic regression were revealed to be the most optimal method of predictions for kidney and heart disease respectively.

scholarly journals Powerful processing to three-dimensional facial recognition using triple information

2020 ◽  
Vol 9 (4) ◽  
pp. 326
Author(s):  
Mohammad Karimi Moridani ◽  
Ahad Karimi Moridani ◽  
Mahin Gholipour
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Principal Component ◽  
Support Vector ◽  
The Face ◽  
Principal Component Analysis Algorithm ◽  
The Difference ◽  
Security Surveillance ◽  
The Trinity

<p><span>Face Detection plays a crucial role in identifying individuals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face. The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing.</span></p>

scholarly journals A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 259 ◽  
Author(s):  
Diana Toledo-Pérez ◽  
Miguel Martínez-Prado ◽  
Roberto Gómez-Loenzo ◽  
Wilfrido Paredes-García ◽  
Juvenal Rodríguez-Reséndiz
Keyword(s):  
Lower Limb ◽  
Classification Accuracy ◽  
Research Work ◽  
Principal Component ◽  
Support Vector ◽  
Semg Signal ◽  
Zero Crossings ◽  
The Difference ◽  
The Right

The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification.

scholarly journals Minimum Barrier Distance-Based Object Descriptor for Visual Tracking

2018 ◽  
Vol 8 (11) ◽  
pp. 2233 ◽  
Author(s):  
Zhengzheng Tu ◽  
Linlin Guo ◽  
Chenglong Li ◽  
Ziwei Xiong ◽  
Xiao Wang
Keyword(s):  
Visual Tracking ◽  
Seed Set ◽  
Background Information ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Tracking Tasks ◽  
Bounding Box ◽  
Benchmark Datasets ◽  
The Difference ◽  
Structured Support Vector Machine

In most visual tracking tasks, the target is tracked by a bounding box given in the first frame. The complexity and redundancy of background information in the bounding box inevitably exist and affect tracking performance. To alleviate the influence of background, we propose a robust object descriptor for visual tracking in this paper. First, we decompose the bounding box into non-overlapping patches and extract the color and gradient histograms features for each patch. Second, we adopt the minimum barrier distance (MBD) to calculate patch weights. Specifically, we consider the boundary patches as the background seeds and calculate the MBD from each patch to the seed set as the weight of each patch since the weight calculated by MBD can represent the difference between each patch and the background more effectively. Finally, we impose the weight on the extracted feature to get the descriptor of each patch and then incorporate our MBD-based descriptor into the structured support vector machine algorithm for tracking. Experiments on two benchmark datasets demonstrate the effectiveness of the proposed approach.

scholarly journals Principal Component Analysis and Support Vector Machine on Ice Entrainment Through a Sluice Gate

2021 ◽  
Author(s):  
Naisheng Liang ◽  
Youcai Tuo ◽  
Yun Deng ◽  
Tianfu He
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Level Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Sluice Gate ◽  
K Nearest Neighbors ◽  
Svm Model ◽  
Optimal Kernel

The entrainment and accumulation of ice floes in front of the sluice gates are closely related to the water transport efficiency and safe operation of the channel during an ice period. A flume study is carried out for a sluice gate with free outflow. A framework of stacking ensemble models is used to analyze the data, which consists of a two-level structure including the principal component analysis (PCA) and the support vector machine (SVM) algorithms. Based on the mechanism of ice floe accumulation, ten input characteristics of the machine learning (ML) model are selected. The PCA method is used to eliminate redundant information. The first principal component, with a contribution rate of 71.76%, and the second principal component, with a contribution of rate 15.64%, are extracted as the inputs of the SVM model, and the state of the floating ice in front of the gate is used to determine the classification labels. The 5-fold cross-validation method is used to train the model. The training results showed that the Gaussian radial basis functions (RBF) were the optimal kernel function. The performance of the developed model is measured using area under curve (AUC), accuracy (Acc) and F1-score (F1) values as statistical indicators. The results showed that the established PCA-SVM model improves the Bernoulli naive Bayes (Bernoulli NB) classifier and K-nearest neighbors’ algorithm (KNN) models. It increasing the AUC value by 11% and 5%, the Acc value by 16% and 17%, and the F1 value by 17% and 2%, respectively.

Multivariate micropollutants analysis in marine waters

1995 ◽  
Vol 32 (9-10) ◽  
pp. 341-348
Author(s):  
V. Librando ◽  
G. Magazzù ◽  
A. Puglisi
Keyword(s):  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Environmental Chemistry ◽  
Sampling Site ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Micropollutants ◽  
Suspended Material ◽  
The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

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