Overview of Functional Data Explorer -Functional Principal Component Analysis and Various Use Cases- (Japanese)The "Functional Data Explorer" (FDE) platform is a platform for analyzing functional data, signal data, and time series data. For function data, you can extract function principal components that represent features and perform dimensionality reduction to discriminate function data and create prediction models.
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Functional Data Explorer Overview and Functional Principal Components Analysis (5:34)
- For functional data, extracting principal components of the function as features of the functional data.
- What kind of things will be explained in usage examples 1 and 2, which will be explained later?
Usage example 1: Sample classification using functional data (16:52)
- Basic usage of Function Data Explorer
- How to classify various functions using functional principal components
Usage example 2: Characteristic value prediction using function data (12:49)
- An example of extracting function principal components from function data and fitting a regression model using 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: Functional Response Experiment Design (11:27)
- An example of extracting function principal components from function data and examining their relationship with factors when using them as responses
- Take advantage of the options in the Functional Design of Experiments Analysis.
Use Case 4: Analysis of Spectral Data (12:29)
- A wavelet model is fitted for the analysis of the spectral data.
- Wavelet design of experiments analysis is used to investigate the relationship between spectral shape and various factors.
Advanced Statistical ModelingEnglishPredictive Modeling and Machine Learning
