Learning Skeleton Stream Patterns with Slow Feature Analysis for Action Recognition

2015 ◽  
pp. 111-121 ◽  
Author(s):  
Yanhu Shan ◽  
Zhang Zhang ◽  
Kaiqi Huang
Keyword(s):  
Action Recognition ◽  
Feature Analysis ◽  
Slow Feature Analysis

DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition

2014 ◽  
Author(s):  
Lin Sun ◽  
Kui Jia ◽  
Tsung-Han Chan ◽  
Yuqiang Fang ◽  
Gang Wang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Analysis ◽  
Slow Feature Analysis

scholarly journals An application of slow feature analysis to the genetic sequences of coronaviruses and influenza viruses

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Anastasios A. Tsonis ◽  
Geli Wang ◽  
Lvyi Zhang ◽  
Wenxu Lu ◽  
Aristotle Kayafas ◽  
...  
Keyword(s):  
Mathematical Method ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Transmission Rate ◽  
Complex Structure ◽  
Mortality Rates ◽  
Influenza Viruses ◽  
Feature Analysis ◽  
Slow Feature Analysis ◽  
Genetic Sequences

Abstract Background Mathematical approaches have been for decades used to probe the structure of DNA sequences. This has led to the development of Bioinformatics. In this exploratory work, a novel mathematical method is applied to probe the DNA structure of two related viral families: those of coronaviruses and those of influenza viruses. The coronaviruses are SARS-CoV-2, SARS-CoV-1, and MERS. The influenza viruses include H1N1-1918, H1N1-2009, H2N2-1957, and H3N2-1968. Methods The mathematical method used is the slow feature analysis (SFA), a rather new but promising method to delineate complex structure in DNA sequences. Results The analysis indicates that the DNA sequences exhibit an elaborate and convoluted structure akin to complex networks. We define a measure of complexity and show that each DNA sequence exhibits a certain degree of complexity within itself, while at the same time there exists complex inter-relationships between the sequences within a family and between the two families. From these relationships, we find evidence, especially for the coronavirus family, that increasing complexity in a sequence is associated with higher transmission rate but with lower mortality. Conclusions The complexity measure defined here may hold a promise and could become a useful tool in the prediction of transmission and mortality rates in future new viral strains.

Structure-Aware Slow Feature Analysis for Age Estimation

2016 ◽  
Vol 23 (12) ◽  
pp. 1702-1706 ◽  
Author(s):  
Zhouzhou He ◽  
Xi Li ◽  
Zhongfei Zhang ◽  
Yaqing Zhang ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Age Estimation ◽  
Feature Analysis ◽  
Slow Feature Analysis

Extracting the driving force from ozone data using slow feature analysis

2015 ◽  
Vol 124 (3-4) ◽  
pp. 985-989 ◽  
Author(s):  
Geli Wang ◽  
Peicai Yang ◽  
Xiuji Zhou
Keyword(s):  
Driving Force ◽  
Feature Analysis ◽  
Slow Feature Analysis ◽  
Ozone Data
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