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Found 6 papers, 2 papers with code
From explained variance of correlated components to PCA without orthogonality constraints
Block Principal Component Analysis (Block PCA) of a data matrix A, where loadings Z are determined by maximization of AZ 2 over unit norm orthogonal loadings, is difficult to use for the design of sparse PCA by 1 regularization, due to the difficulty of taking care of both the orthogonality constraint on loadings and the non differentiable 1 penalty.
ClustGeo: an R package for hierarchical clustering with spatial constraints
In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints.
Optimal Projected Variance Group-Sparse Block PCA
We address the problem of defining a group sparse formulation for Principal Components Analysis (PCA) - or its equivalent formulations as Low Rank approximation or Dictionary Learning problems - which achieves a compromise between maximizing the variance explained by the components and promoting sparsity of the loadings.
Combining clustering of variables and feature selection using random forests
Numerical performances of the proposed approach are compared with direct applications of random forests and variable selection using random forests on the original p variables.
Statistics Theory Statistics Theory
Multivariate Analysis of Mixed Data: The R Package PCAmixdata
Mixed data arise when observations are described by a mixture of numerical and categorical variables.
Computation
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms
We propose a novel class of algorithms for low rank matrix completion.
