The Linear Discriminant Analysis (LDA) is a classification algorithm, used in machine learning and pattern detection. For linearly classifiable data, it classifies the data faster than the Support Vector Machine. It also allows for dimensionality reduction, if required. It is used if two or more features convey the same information; for optimizing, the redundant features are not used in deciding the classification criteria. Basically, the LDA maximizes the ratio of the between-class variance to the within-class variance.
For practical purposes, the LDA seems to be used more often than the SVM. So we tried this classification method on our data. We obtained the following results for a Single Sensor LDA.
For practical purposes, the LDA seems to be used more often than the SVM. So we tried this classification method on our data. We obtained the following results for a Single Sensor LDA.
LDA classification results
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