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Found 2 papers, 1 papers with code
The Geometry of Nonlinear Embeddings in Kernel Discriminant Analysis
We study the geometry of nonlinear embeddings in discriminant analysis with polynomial kernels and Gaussian kernel by identifying the population-level discriminant function that depends on the data distribution and the kernel.
Dimensionality Reduction for Binary Data through the Projection of Natural Parameters
We propose a new formulation of logistic PCA which extends Pearson's formulation of a low dimensional data representation with minimum error to binary data.
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