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Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction
[article]
2020
arXiv
pre-print
Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data
arXiv:2001.03103v2
fatcat:kw4whldrfzguzpo5ldxyu3ndyi