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Multi-Objective Equilibrium Optimizer for Feature Selection in high-dimensional English Speech Emotion Recognition

Computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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Coherence migration in high-dimensional bipartite systems

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Chinese Physics B 2022年第6期31卷 228-234页

作者：丁智勇周攀峰范小刚刘程程何娟叶柳 School of Physics and Electronic Engineering Fuyang Normal UniversityFuyang 236037China Key Laboratory of Functional Materials and Devices for Informatics of Anhui Educational Institutions Fuyang Normal UniversityFuyang 236037China School of Physics and Optoelectronics Engineering Anhui UniversityHefei 230039China

The conservation law for first-order coherence and mutual correlation of a bipartite qubit state was firstly proposed by Svozil′?k et al.,and their theories laid the foundation for the study of coherence migration under unitary transformations.In this paper,we generalize the framework of first-order coherence and mutual correlation to an arbitrary(m■n)-dimensional bipartite composite state by introducing an extended Bloch decomposition form of the state.We also generalize two kinds of unitary operators in high-dimensional systems,which can bring about coherence migration and help to obtain the maximum or minimum first-order coherence.Meanwhile,the coherence migration in open quantum systems is investigated.We take depolarizing channels as examples and establish that the reduced first-order coherence of the principal system over time is completely transformed into mutual correlation of the(2■4)-dimensional system-environment bipartite composite state.It is expected that our results may provide a valuable idea or method for controlling the quantum resource such as coherence and quantum correlations.

关键词： first-order coherence mutual correlation coherence migration high-dimensional

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Finite-key analysis of practical time-bin high-dimensional quantum key distribution with afterpulse effect

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Chinese Physics B 2022年第8期31卷 234-240页

作者：周雨周淳汪洋陆宜飞江木生张晓旭鲍皖苏 Henan Key Laboratory of Quantum Information and Cryptography SSF IEUZhengzhou 450001China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China

high-dimensional quantum resources provide the ability to encode several bits of information on a single photon,which can particularly increase the secret key rate rate of quantum key distribution(QKD) systems. Recently, a practical four-dimensional QKD scheme based on time-bin quantum photonic state, only with two single-photon avalanche detectors as measurement setup, has been proven to have a superior performance than the qubit-based one. In this paper, we extend the results to our proposed eight-dimensional scheme. Then, we consider two main practical factors to improve its secret key bound. Concretely, we take the afterpulse effect into account and apply a finite-key analysis with the intensity fluctuations.Our secret bounds give consideration to both the intensity fluctuations and the afterpulse effect for the high-dimensional QKD systems. Numerical simulations show the bound of eight-dimensional QKD scheme is more robust to the intensity fluctuations but more sensitive to the afterpulse effect than the four-dimensional one.

关键词： high-dimensional time-bin finite-key analysis intensity fluctuations afterpulse effect

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Differentially private high-dimensional data publication via grouping and truncating techniques

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Frontiers of Computer Science 2019年第2期13卷 382-395页

作者： Ning WANG Yu GU Jia XU Fangfang LI Ge YU School of Computer Science and Engineering Northeastern University Shenyang 110819 China School of Computer Electronics and Information Guangxi University Guangxi 530004 China

The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in nuinerous applications such as analyzing popular spots based on check-in location information and mining valuable items from shopping records. However, this poses a privacy threat when directly publishing this information. Differential privacy (DP), as a notable paradigm for strong privacy guarantees, is thereby adopted to publish all column counts. Prior studies have verified that truncating records or grouping columns can effectively improve the accuracy of published results. To leverage the advantages of the two techniques, we combine these studies to further boost the accuracy of published results. However, the traditional penalty function, which measures the error imported by a given pair of parameters including truncating length and group size, is so sensitive that the derived parameters deviate from the optimal parameters significantly. To output preferable parameters, we first design a smart penalty function that is less sensitive than the traditional function. Moreover, a two-phase selection method is proposed to compute these parameters efficiently, together with the improvement in accuracy. Extensive experiments on a broad spectrum of real-world datasets validate the effectiveness of our proposals.

关键词： differential privacy high-dimensional data truncation optimization grouping penalty function

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Empirical Likelihood of Quantile Difference with Missing Response When high-dimensional Covariates Are Present

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Acta Mathematica Sinica,English Series 2021年第12期37卷 1803-1825页

作者： Cui Juan KONG Han Ying LIANG School of Mathematical Sciences Tongji UniversityShanghai 200092P.R.China

We,in this paper,investigate two-sample quantile difference by empirical likelihood method when the responses with high-dimensional covariates of the two populations are missing at random.In particular,based on sufficient dimension reduction technique,we construct three empirical log-likelihood ratios for the quantile difference between two samples by using inverse probability weighting imputation,regression imputation as well as augmented inverse probability weighting imputation,respectively,and prove their asymptotic distributions.At the same time,we give a test to check whether two populations have the same distribution.A simulation study is carried out to investigate finite sample behavior of the proposed methods too.

关键词： Empirical likelihood high-dimensional missing at random sufficient dimension reduction two-sample quantile difference

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Efficient Distributed Estimation of high-dimensional Sparse Precision Matrix for Transelliptical Graphical Models

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Acta Mathematica Sinica,English Series 2021年第5期37卷 689-706页

作者： Guan Peng WANG Heng Jian CUI School of Mathematical Sciences Capital Normal UniversityBeijing 100048P.R.China

In this paper,distributed estimation of high-dimensional sparse precision matrix is proposed based on the debiased D-trace loss penalized lasso and the hard threshold method when samples are distributed into different machines for transelliptical graphical models.At a certain level of sparseness,this method not only achieves the correct selection of non-zero elements of sparse precision matrix,but the error rate can be comparable to the estimator in a non-distributed setting.The numerical results further prove that the proposed distributed method is more effective than the usual average method.

关键词： Distributed estimator sparse precision matrix high-dimensional hard threshold efficient communication

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A combined p-value test for the mean difference of high-dimensional data

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Science China Mathematics 2019年第5期62卷 961-978页

作者： Wei Yu Wangli Xu Lixing Zhu Center for Applied Statistics School of Statistics Renmin University of China School of Statistics Beijing Normal University Department of Mathematics Hong Kong Baptist University

This paper proposes a novel method for testing the equality of high-dimensional means using a multiple hypothesis test. The proposed method is based on the maximum of standardized partial sums of logarithmic p-values statistic. Numerical studies show that the method performs well for both normal and non-normal data and has a good power performance under both dense and sparse alternative hypotheses. For illustration, a real data analysis is implemented.

关键词： high-dimensional data equality of means multiple hypothesis testing sparse alternatives

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A Sequence Image Matching Method Based on Improved high-dimensional Combined Features

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Transactions of Nanjing University of Aeronautics and Astronautics 2018年第5期35卷 820-828页

作者： Leng Xuefei Gong Zhe Fu Runzhe Liu Yang College of Astronautics Nanjing University of Aeronautics and Astronautics

Image matching technology is theoretically significant and practically promising in the field of autonomous navigation.Addressing shortcomings of existing image matching navigation technologies,the concept of high-dimensional combined feature is presented based on sequence image matching navigation.To balance between the distribution of high-dimensional combined features and the shortcomings of the only use of geometric relations,we propose a method based on Delaunay triangulation to improve the feature,and add the regional characteristics of the features together with their geometric characteristics.Finally,k-nearest neighbor(KNN)algorithm is adopted to optimize searching process.Simulation results show that the matching can be realized at the rotation angle of-8°to 8°and the scale factor of 0.9 to 1.1,and when the image size is 160 pixel×160 pixel,the matching time is less than 0.5 s.Therefore,the proposed algorithm can substantially reduce computational complexity,improve the matching speed,and exhibit robustness to the rotation and scale changes.

关键词： sequence image matching navigation Delaunay triangulation high-dimensional combined feature k-nearest neighbor

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K-Hyperparameter Tuning in high-dimensional Space Clustering:Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

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Journal on Artificial Intelligence 2023年第1期5卷 75-112页

作者： Rufus Gikera Jonathan Mwaura Elizaphan Muuro Shadrack Mambo Department of Computer Science Riara UniversityNairobi00200Kenya Department of Computing Khoury College of Computer SciencesBoston02115USA Department of Engineering Kenyatta UniversityNairobi00200Kenya

k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets.However,one of its setbacks is the challenge of identifying the correct k-hyperparameter value.Tuning this value correctly is critical for building effective k-means models.The use of the traditional elbow method to help identify this value has a long-standing literature.However,when using this method with certain datasets,smooth curves may appear,making it challenging to identify the k-value due to its unclear nature.On the other hand,various internal validation indexes,which are proposed as a solution to this issue,may be inconsistent.Although various techniques for solving smooth elbow challenges exist,k-hyperparameter tuning in high-dimensional spaces still remains intractable and an open research issue.In this paper,we have first reviewed the existing techniques for solving smooth elbow challenges.The identified research gaps are then utilized in the development of the new technique.The new technique,referred to as the ensemble-based technique of a self-adapting autoencoder and internal validation indexes,is then validated in high-dimensional space clustering.The optimal k-value,tuned by this technique using a voting scheme,is a trade-off between the number of clusters visualized in the autoencoder’s latent space,k-value from the ensemble internal validation index score and one that generates a value of 0 or close to 0 on the derivative f″′(k)(1+f′(k)^(2))−3 f″(k)^(2)f″((k)2f′(k),at the elbow.Experimental results based on the Cochran’s Q test,ANOVA,and McNemar’s score indicate a relatively good performance of the newly developed technique in k-hyperparameter tuning.

关键词： k-hyperparameter tuning high-dimensional smooth elbow

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Outlier Detection Model Based on Autoencoder and Data Augmentation for high-dimensional Sparse Data

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国际计算机前沿大会会议论文集 2023年第1期 192-206页

作者： Haitao Zhang Wenhai Ma Qilong Han Zhiqiang Ma College of Computer Science and Technology Harbin Engineering UniversityHarbinChina

This paper aims to address the problems of data imbalance,parame-ter adjustment complexity,and low accuracy in high-dimensional data anomaly detection.To address these issues,an autoencoder and data augmentation-based anomaly detection model for high-dimensional sparse data is proposed(SEAOD).First,the model solves the problem of imbalanced data by using the weighted SMOTE algorithm and ENN algorithm toﬁll in the minority class samples and generate a new dataset.Then,an attention mechanism is employed to calculate the feature similarity and determine the structure of the neural network so that the model can learn the data features.Finally,the data are dimensionally reduced based on the autoencoder,and the sparse high-dimensional data are mapped to a low-dimensional space for anomaly detection,overcoming the impact of the curse of dimensionality on detection algorithms.The experimental results show that on 15 public datasets,this model outperforms other comparison algorithms.Furthermore,it was validated on industrial air quality datasets and achieved the expected results with practicality.

关键词： high-dimensional data augmentation attention mechanism Outlier Detection

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