Clustering of variables is the task grouping similar into different groups. It may be useful in several situations such as dimensionality reduction, feature selection, and detect redundancies. In present study, we combine two methods features clustering around latent (CLV) algorithm k-means based co-clustering (kCC). Indeed, classical CLV cannot applied to high dimensional data because this app...

We prove a universality theorem for learning with random features. Our result shows that, in terms of training and generalization errors, feature model nonlinear activation function is asymptotically equivalent to surrogate linear Gaussian matching covariance matrix. This settles so-called equivalence conjecture based on which several recent papers develop their results. method proving the buil...

We propose an efficient solution approach for high-dimensional nonlocal mean-field game (MFG) systems based on the Monte Carlo approximation of interaction kernels via random features. avoid costly space-discretizations terms in state-space by passing to feature-space. This allows a seamless extension virtually any single-agent trajectory optimization algorithm. Here, we extend direct transcrip...