Novel Canonical Correlation Analysis Based Feature Level Fusion Algorithm for Multimodal Recognition in Biometric Sensor Systems

2019 ◽  
Vol 17 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Chetana Kamlaskar ◽  
Shubhangi Deshmukh ◽  
Suresh Gosavi ◽  
Aditya Abhyankar
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Sensor Systems ◽  
Fusion Algorithm ◽  
Feature Level Fusion ◽  
Level Fusion

scholarly journals Robust Multi-View Representation Learning (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13939-13940
Author(s):  
Sibi Venkatesan ◽  
James K. Miller ◽  
Artur Dubrawski
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Medical Patient ◽  
Representation Learning ◽  
Sensor Systems ◽  
Group Sparsity ◽  
Self Driving Cars ◽  
Patient Side ◽  
Straightforward Extension

Multi-view data has become ubiquitous, especially with multi-sensor systems like self-driving cars or medical patient-side monitors. We propose two methods to approach robust multi-view representation learning with the aim of leveraging local relationships between views.The first is an extension of Canonical Correlation Analysis (CCA) where we consider multiple one-vs-rest CCA problems, one for each view. We use a group-sparsity penalty to encourage finding local relationships. The second method is a straightforward extension of a multi-view AutoEncoder with view-level drop-out.We demonstrate the effectiveness of these methods in simple synthetic experiments. We also describe heuristics and extensions to improve and/or expand on these methods.

Analysis of Maximum Expiratory Flow Volume Curves Using Canonical Correlation Analysis

1985 ◽  
Vol 24 (02) ◽  
pp. 91-100 ◽  
Author(s):  
W. van Pelt ◽  
Ph. H. Quanjer ◽  
M. E. Wise ◽  
E. van der Burg ◽  
R. van der Lende
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Flow Volume ◽  
Non Linear ◽  
Maximum Expiratory Flow ◽  
Expiratory Flow ◽  
Relationship Of ◽  
The Relationship ◽  
Age And Sex

SummaryAs part of a population study on chronic lung disease in the Netherlands, an investigation is made of the relationship of both age and sex with indices describing the maximum expiratory flow-volume (MEFV) curve. To determine the relationship, non-linear canonical correlation was used as realized in the computer program CANALS, a combination of ordinary canonical correlation analysis (CCA) and non-linear transformations of the variables. This method enhances the generality of the relationship to be found and has the advantage of showing the relative importance of categories or ranges within a variable with respect to that relationship. The above is exemplified by describing the relationship of age and sex with variables concerning respiratory symptoms and smoking habits. The analysis of age and sex with MEFV curve indices shows that non-linear canonical correlation analysis is an efficient tool in analysing size and shape of the MEFV curve and can be used to derive parameters concerning the whole curve.

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