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What is High Dimensional Data Feature Selection about?
Recently available data from applications like text mining and computer vision has increased exponentially in volume and dimensionality. Feature selection techniques aim to choose a small subset of relevant features by removing noisy, redundant and irrelevant dimensions. Feature selection methods can be classified as supervised, semi-supervised, or unsupervised depending on the availability of label information to select discriminative features and guide the selection process.
- Author
- Max Planck
- Language
- EN