scholarly journals A machine learning approach to portfolio pricing and risk management for high-dimensional problems

2020 ◽  
Author(s):  
Lucio Fernandez Arjona ◽  
Damir Filipovic
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
High Dimensional ◽  
Learning Approach ◽  
Machine Learning Approach

Novel machine learning approach for classification of high-dimensional microarray data

2019 ◽  
Vol 23 (24) ◽  
pp. 13409-13421 ◽  
Author(s):  
Rabia Aziz Musheer ◽  
C. K. Verma ◽  
Namita Srivastava
Keyword(s):  
Machine Learning ◽  
Microarray Data ◽  
High Dimensional ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort

2020 ◽  
Vol 11 ◽  
Author(s):  
Julianne Duhazé ◽  
Signe Hässler ◽  
Delphine Bachelet ◽  
Aude Gleizes ◽  
Salima Hacein-Bey-Abina ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Dimensional ◽  
Learning Approach ◽  
Time To Event ◽  
Event Prediction ◽  
Machine Learning Approach

scholarly journals Two-Stage Latent Dynamics Modeling and Filtering for Characterizing Individual Walking and Running Patterns with Smartphone Sensors

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2712 ◽  
Author(s):  
Jaein Kim ◽  
Juwon Lee ◽  
Woongjin Jang ◽  
Seri Lee ◽  
Hongjoong Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Variables ◽  
High Dimensional ◽  
Learning Approach ◽  
Dynamic Walking ◽  
Dynamics Modeling ◽  
Two Stage ◽  
Human Movements ◽  
Latent Space ◽  
Machine Learning Approach

Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as ’two-stage latent dynamics modeling and filtering’ (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns.

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