Comparative Analysis of Dimension Reduction Techniques Over Classification Algorithms for Speech Emotion Recognition

2020 ◽  
pp. 170-184
Author(s):  
Aditi Biswas ◽  
Sovon Chakraborty ◽  
Abu Nuraiya Mahfuza Yesmin Rifat ◽  
Nadia Farhin Chowdhury ◽  
Jia Uddin
Keyword(s):  
Comparative Analysis ◽  
Dimension Reduction ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Classification Algorithms ◽  
Reduction Techniques ◽  
Dimension Reduction Techniques

scholarly journals Detection of DoS Attacks in an IoT Environment with MQTT Protocol Based on Intelligent Binary Classifiers

2021 ◽  
Vol 7 (1) ◽  
pp. 16
Author(s):  
Álvaro Michelena ◽  
Francisco Zayas-Gato ◽  
Esteban Jove ◽  
José Luis Calvo-Rolle
Keyword(s):  
Dimension Reduction ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Classification Algorithms ◽  
Dos Attacks ◽  
Reduction Techniques ◽  
Binary Classifiers ◽  
Dimension Reduction Techniques

The present work deals with the problem of detecting Denial of Service attacks in an IoT environment. To achieve this goal, a dataset registered in an MQTT protocol network is used, applying dimension reduction techniques combined with classification algorithms. The final classifiers presents successful results.

Comparative Analysis of Linear and Nonlinear Dimension Reduction Techniques on Mass Cytometry Data

2018 ◽  
Author(s):  
Anna Konstorum ◽  
Nathan Jekel ◽  
Emily Vidal ◽  
Reinhard Laubenbacher
Keyword(s):  
Comparative Analysis ◽  
Dimension Reduction ◽  
Residual Variance ◽  
Two Dimensional ◽  
Diffusion Maps ◽  
Protein Markers ◽  
Mass Cytometry ◽  
Reduction Techniques ◽  
Dimension Reduction Techniques ◽  
And Diffusion

AbstractMass cytometry, also known as CyTOF, is a newly developed technology for quantification and classification of immune cells that can allow for analysis of over three dozen protein markers per cell. The high dimensional data that is generated requires innovative methods for analysis and visualization. We conducted a comparative analysis of four dimension reduction techniques – principal component analysis (PCA), isometric feature mapping (Isomap), t-distributed stochastic neighbor embedding (t-SNE), and Diffusion Maps by implementing them on benchmark mass cytometry data sets. We compare the results of these reductions using computation time, residual variance, a newly developed comparison metric we term neighborhood proportion error (NPE), and two-dimensional visualizations. We find that t-SNE and Diffusion Maps are the two most effective methods for preserving relationships of interest among cells and providing informative visualizations. In low dimensional embeddings, t-SNE exhibits well-defined phenotypic clustering. Additionally, Diffusion Maps can represent cell differentiation pathways with long projections along each diffusion component. We thus recommend a complementary approach using t-SNE and Diffusion Maps in order to extract diverse and informative cell relationship information in a two-dimensional setting from CyTOF data.

scholarly journals An Improved Process for Generating Uniform PSSMs and Its Application in Protein Subcellular Localization via Various Global Dimension Reduction Techniques

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 42384-42395 ◽  
Author(s):  
Shunfang Wang ◽  
Wenjia Li ◽  
Yu Fei ◽  
Zicheng Cao ◽  
Dongshu Xu ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
Dimension Reduction ◽  
Global Dimension ◽  
Protein Subcellular Localization ◽  
Reduction Techniques ◽  
Dimension Reduction Techniques

scholarly journals Comparing Dimension Reduction Techniques for Document Clustering

2005 ◽  
pp. 292-296 ◽  
Author(s):  
Bin Tang ◽  
Michael Shepherd ◽  
Malcolm I. Heywood ◽  
Xiao Luo
Keyword(s):  
Dimension Reduction ◽  
Document Clustering ◽  
Reduction Techniques ◽  
Dimension Reduction Techniques
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