EVRI-thing You Need to Know About How to do Principal Components Analysis
April 6, 2021
Principal Components Analysis (PCA) is the most fundamental and perhaps most important single technique in pattern recognition and machine learning. It is used by itself for viewing and understanding the structure of highly multivariate data, for the identification of outliers and as the basis for Multivariate Statistical Process Control (MSPC). It is also used in sample classification as well as for preprocessing and compression in machine learning methods.
Eigenvector Technology Principal Bob Roginski will show you how to use Eigenvector’s MATLAB based PLS_Toolbox or stand-alone Solo to do PCA so you can start using this important method for your own data discovery. In this webinar we’ll discuss:
- Importing data into the Workspace Browser
- The Analysis interface for PCA
- Basic data preprocessing
- Viewing scores and loadings
- Outlier statistics Q and T^2
- The biplot for combining scores and loadings
- Applying PCA models to new data
- Saving PCA models
Register for “EVRI-thing You Need to Know About How to do Principal Components Analysis” through your existing Eigenvector account, or create one. The webinar is free, but you must register to attend. Find the webinar near the bottom of the page under the “Purchase” tab.
A question and answer session will follow the webinar. Don’t miss your chance to quiz Bob and the Eigen-Guys about the ins and outs of PCA.
Reserve your seat today. The webinar will be live on Tuesday, April 6, at 7:00am PDT, (that’s 16:00 CEST). We will send you a WebEx invitation the day before the webinar. We hope you can attend live, but if you sign up and can’t make it, we’ll send you a link to view the recording the day after the webinar.