Data analysis using principal component analysis
- Resource Type
- Conference
- Authors
- Sehgal, Shruti; Singh, Harpreet; Agarwal, Mohit; Bhasker, V.; Shantanu
- Source
- 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom) Medical Imaging, m-Health and Emerging Communication Systems (MedCom), 2014 International Conference on. :45-48 Nov, 2014
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Principal component analysis
Covariance matrices
Sensors
Compounds
Equations
Mathematical model
Vectors
Data Analysis
Eigen
PCA
- Language
In this paper, we have evaluated an algorithm using Principal Component Analysis (PCA) for its application in data analysis. In the research field, it is very difficult to understand the large amount of data and is very time consuming too. Therefore, in order to avoid wastage of time and for the ease in understanding we have scrutinized a PCA algorithm that can reduce the huge dimension of the data into 2-dimensional. The method of PCA is used to compress the maximum amount of information into first two columns of the transformed matrix known as the principal components by neglecting the other vectors that carries the negligible information or redundant data. The main objective of the paper is to separate two compounds say A and B having different concentrations for all four sensors and identifies which sensors have the similar or different concentration with the help of various plots that explains the correlation between the different variables.