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[HTML][HTML] A Comprehensive Review on Discriminant Analysis for Addressing Challenges of Class-Level Limitations, Small Sample Size, and Robustness
The classical linear discriminant analysis (LDA) algorithm has three primary drawbacks, ie,
small sample size problem, sensitivity to noise and outliers, and inability to deal with multi …
small sample size problem, sensitivity to noise and outliers, and inability to deal with multi …
Tensor attention training: Provably efficient learning of higher-order transformers
Y Liang, Z Shi, Z Song, Y Zhou - ar**
PP Markopoulos, S Kundu… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
It was shown recently that the K L1-norm principal components (L1-PCs) of a real-valued
data matrix X∈ RD× N (N data samples of D dimensions) can be exactly calculated with cost …
data matrix X∈ RD× N (N data samples of D dimensions) can be exactly calculated with cost …
Fisher discriminant analysis with L1-norm
Fisher linear discriminant analysis (LDA) is a classical subspace learning technique of
extracting discriminative features for pattern recognition problems. The formulation of the …
extracting discriminative features for pattern recognition problems. The formulation of the …
Linear discriminant analysis based on L1-norm maximization
Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique,
which is widely used for many purposes. However, conventional LDA is sensitive to outliers …
which is widely used for many purposes. However, conventional LDA is sensitive to outliers …
Relative error tensor low rank approximation
We consider relative error low rank approximation of tensors with respect to the Frobenius
norm. Namely, given an order-q tensor A∊ ℝ∏ i= 1 q ni, output a rank-k tensor B for which …
norm. Namely, given an order-q tensor A∊ ℝ∏ i= 1 q ni, output a rank-k tensor B for which …