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Found 6 papers, 0 papers with code
Principal Component Analysis Based on T$\ell_1$-norm Maximization
Classical principal component analysis (PCA) may suffer from the sensitivity to outliers and noise.
Multiple Flat Projections for Cross-manifold Clustering
In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems.
Single and Union Non-parallel Support Vector Machine Frameworks
The other type constructs all the hyperplanes simultaneously, and it solves one big optimization problem with the ascertained loss of each sample.
Robust Bhattacharyya bound linear discriminant analysis through adaptive algorithm
In this paper, we propose a novel linear discriminant analysis criterion via the Bhattacharyya error bound estimation based on a novel L1-norm (L1BLDA) and L2-norm (L2BLDA).
Generalized two-dimensional linear discriminant analysis with regularization
However, 2DLDA may encounter the singularity issue theoretically and the sensitivity to outliers.
Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems
In this paper, stochastic gradient descent algorithm is investigated to twin support vector machines for classification.
