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Found 10 papers, 0 papers with code
Generalized two-dimensional linear discriminant analysis with regularization
However, 2DLDA may encounter the singularity issue theoretically and the sensitivity to outliers.
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).
Ramp-based Twin Support Vector Clustering
Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost.
A general model for plane-based clustering with loss function
In this paper, we propose a general model for plane-based clustering.
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.
Principal Component Analysis Based on T$\ell_1$-norm Maximization
Classical principal component analysis (PCA) may suffer from the sensitivity to outliers and noise.
Capped norm linear discriminant analysis and its applications
To improve the robustness of LDA, in this paper, we introduce capped l_{2, 1}-norm of a matrix, which employs non-squared l_2-norm and "capped" operation, and further propose a novel capped l_{2, 1}-norm linear discriminant analysis, called CLDA.
Two-dimensional Bhattacharyya bound linear discriminant analysis with its applications
Recently proposed L2-norm linear discriminant analysis criterion via the Bhattacharyya error bound estimation (L2BLDA) is an effective improvement of linear discriminant analysis (LDA) for feature extraction.
Nonlinear Kernel Support Vector Machine with 0-1 Soft Margin Loss
Recent advance on linear support vector machine with the 0-1 soft margin loss ($L_{0/1}$-SVM) shows that the 0-1 loss problem can be solved directly.
Learning using granularity statistical invariants for classification
Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems.
