The "Functional Data Explorer" (FDE) platform is a platform for analyzing function data, signal data, and time series data. It can extract principal components that represent the characteristics of function data, reduce the dimensions of the data, and perform functions such as discriminating between function data and creating predictive models.
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Functional Data Explorer Overview and Functional Principal Component Analysis (5:34)
- For functional data, extracting the principal components of functions as the features of the functional data.
- What will be explained in Usage Examples 1 and 2 below?
Use case 1: Classifying samples using function data (16 minutes 52 seconds)
- Basic usage of Function Data Explorer
- How to classify various functions using function principal components
Use case 2: Predicting characteristic values using function data (12 minutes 49 seconds)
- An example of extracting function principal components from function data and fitting a regression model with the function principal components as explanatory variables and characteristic values as objective variables.
- Explore the shape of the function that affects the variability of the response values.
Use Case 3: Function Response Experimental Design (11:27)
- An example of extracting functional principal components from functional data and examining the relationship between the responses and factors.
- Use the options in Functional Experimental Design Analysis.
Use Case 4: Spectral Data Analysis (12:29)
- For the analysis of the spectral data, a wavelet model is fitted.
- Wavelet experimental design analysis is used to investigate the relationship between the shape of the spectrum and various factors.
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