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Found 13 papers, 2 papers with code
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation
Then, we propose a practical method through outlier-robust density ratio estimation, which can be seen as performing maximization of MI, nonlinear ICA or nonlinear subspace estimation.
Robust contrastive learning and nonlinear ICA in the presence of outliers
We develop two robust nonlinear ICA methods based on the {\gamma}-divergence, which is a robust alternative to the KL-divergence in logistic regression.
Robust modal regression with direct log-density derivative estimation
In order to apply a gradient method for the maximization, the fundamental challenge is accurate approximation of the gradient of MRR, not MRR itself.
Neural-Kernelized Conditional Density Estimation
Among existing methods, non-parametric and/or kernel-based methods are often difficult to use on large datasets, while methods based on neural networks usually make restrictive parametric assumptions on the probability densities.
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Here, we propose a general framework for nonlinear ICA, which, as a special case, can make use of temporal structure.
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
Based on the proposed estimator, novel methods both for mode-seeking clustering and density ridge estimation are developed, and the respective convergence rates to the mode and ridge of the underlying density are also established.
Whitening-Free Least-Squares Non-Gaussian Component Analysis
Non-Gaussian component analysis (NGCA) is an unsupervised linear dimension reduction method that extracts low-dimensional non-Gaussian "signals" from high-dimensional data contaminated with Gaussian noise.
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Non-Gaussian component analysis (NGCA) is aimed at identifying a linear subspace such that the projected data follows a non-Gaussian distribution.
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction
On the other hand, quadratic MI (QMI) is a variant of MI based on the $L_2$ distance which is more robust against outliers than the KL divergence, and a computationally efficient method to estimate QMI from data, called least-squares QMI (LSQMI), has been proposed recently.
Regularized Multi-Task Learning for Multi-Dimensional Log-Density Gradient Estimation
Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring non-Gaussianity.
Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure
The precision matrix of the linear components is assumed to be randomly generated by a higher-order process and explicitly parametrized by a parameter matrix.
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation
Estimation of density derivatives is a versatile tool in statistical data analysis.
Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density
We then develop a mean-shift-like fixed-point algorithm to find the modes of the density for clustering.
