arXiv research

Proposes an online method for high-dimensional streaming data.

This paper proposes a novel kernel approach to linear dimension reduction for supervised learning. The purpose of the dimension reduction is to find directions in the input space to explain the output as effectively as possible. The proposed method uses an estimator for the gradient of regression function, based on the…

New method for reducing dimensions of distributional data.

Survey of SDR methods for high-dimensional regression and embedding.

Optimizes differentially private kernel learning with random projection.

A new deep neural network tackles nonlinear functional regression with improved dimensionality reduction.

In statistical learning, high covariate dimensionality poses challenges for robust prediction and inference. To address this challenge, supervised dimension reduction is often performed, where dependence on the outcome is maximized for a selected covariate subspace with smaller dimensionality. Prevalent dimension reduc…

Kernel PCA helps analyze multivariate extremes and clusters them effectively.

The purpose of sufficient dimension reduction (SDR) is to find the low-dimensional subspace of input features that is sufficient for predicting output values. In this paper, we propose a novel distribution-free SDR method called sufficient component analysis (SCA), which is computationally more efficient than existing …

Paper compares dimension reduction methods using topological analysis on EEG data.

Study shows how to effectively predict functions on manifolds using kernel methods.

GDMaps reduces high-dimensional data to lower dimensions for better classification.

New method circumvents curse of dimensionality in Laplacian estimation.

New bounds on KPCA efficiency reveal conditions for fast convergence.

Study on reducing dimensionality in high-dimensional regression with kernel methods and stability analysis.

A new geometry-preserving method for interpreting compositional data.

We propose a method for feature selection that employs kernel-based measures of independence to find a subset of covariates that is maximally predictive of the response. Building on past work in kernel dimension reduction, we show how to perform feature selection via a constrained optimization problem involving the tra…

Develops a nonparametric graphical model for conditional independence.

In this paper, we propose a novel supervised learning method that is called Deep Embedding Kernel (DEK). DEK combines the advantages of deep learning and kernel methods in a unified framework. More specifically, DEK is a learnable kernel represented by a newly designed deep architecture. Compared with pre-defined kerne…

New algorithms for clustering and dimension reduction using relative von Neumann entropy.

Let $(X, T^{1,0}X)$ be a compact connected orientable CR manifold of dimension $2n+1$ with non-degenerate Levi curvature. Assume that $X$ admits a connected compact Lie group action $G$. Under certain natural assumptions about the group action $G$, we show that the $G$-invariant Szegö kernel for $(0,q)$ forms is a comp…

New insights into tSNE for large datasets.

The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence measure that has applications in various aspects of machine learning. Conveniently, the objectives of different dimensionality reduction applications using HSIC often reduce to the same optimization problem. However, the nonconvexity of the object…

Bayesian optimization (BO) has been broadly applied to computational expensive problems, but it is still challenging to extend BO to high dimensions. Existing works are usually under strict assumption of an additive or a linear embedding structure for objective functions. This paper directly introduces a supervised dim…

EnEMF uses Epanechnikov kernel for high-dimensional filtering, improving accuracy and robustness.

Paper develops KMS Wasserstein for high-dimensional data reduction.

A new method for fair representation learning using PLS.

Overview of geometric analysis for manifold learning.

A method for reducing dimensions in Fréchet regression models.

Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable of capturing nonlinear relationships. The standard strategy is to first map the data into a high dimensional feature space using kernels prior to a projec…

Unified framework for spectral methods, kernel learning, and manifold unfolding.

Survey of kernels, RKHS, and their applications in machine learning.

Because of high dimensionality, correlation among covariates, and noise contained in data, dimension reduction (DR) techniques are often employed to the application of machine learning algorithms. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and their kernel variants (KPCA, KLDA) are among th…

DM uses semigroup property to tune diffusion time for better data analysis.

We propose a representation of Gaussian processes (GPs) based on powers of the integral operator defined by a kernel function, we call these stochastic processes integral Gaussian processes (IGPs). Sample paths from IGPs are functions contained within the reproducing kernel Hilbert space (RKHS) defined by the kernel fu…

Identifies a gradient flow to solve kernel learning problems with noise reduction.

Study pure exploration in high-dimensional feature spaces using adaptive embeddings.

Dimensionality reduction (DR) on the manifold includes effective methods which project the data from an implicit relational space onto a vectorial space. Regardless of the achievements in this area, these algorithms suffer from the lack of interpretation of the projection dimensions. Therefore, it is often difficult to…

Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in Gaussian Processes (GPs) and incorporate GPs as components of larger graphical models. Nonetheless, the standard GPLVM variational inference approach is tractable only for a narrow family of kernel functions. The most p…

New method corrects missing data bias in dimension reduction.

New method for spatiotemporal data regression using Gaussian processes.

New kernels allow learning from non-separable data.

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly du…

Sensor data analysis plays a key role in health assessment of critical equipment. Such data are multivariate and exhibit nonlinear relationships. This paper describes how one can exploit nonlinear dimension reduction techniques, such as the t-distributed stochastic neighbor embedding (t-SNE) and kernel principal compon…

Sparse model for noisy datasets using hierarchical regularization.

Dimensionality reduction is an important step in processing the hyperspectral images (HSI) to overcome the curse of dimensionality problem. Linear dimensionality reduction methods such as Independent component analysis (ICA) and Linear discriminant analysis (LDA) are commonly employed to reduce the dimensionality of HS…

One of the major problems in natural language processing (NLP) is the word sense disambiguation (WSD) problem. It is the task of computationally identifying the right sense of a polysemous word based on its context. Resolving the WSD problem boosts the accuracy of many NLP focused algorithms such as text classification…

Unified quadrature framework for large-scale kernel machines.