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Found 19 papers, 4 papers with code
Elliptically-Contoured Tensor-variate Distributions with Application to Improved Image Learning
Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate when data comes from distributions with heavier or lighter tails.
Exploratory Factor Analysis of Data on a Sphere
Data on high-dimensional spheres arise frequently in many disciplines either naturally or as a consequence of preliminary processing and can have intricate dependence structure that needs to be understood.
Fully Three-dimensional Radial Visualization
We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets.
Quantitative Matching of Forensic Evidence Fragments Utilizing 3D Microscopy Analysis of Fracture Surface Replicas
In this study, a statistical analysis comparison protocol was applied to a set of 3D topological images of fractured surface pairs and their replicas to provide confidence in the quantitative statistical comparison between fractured items and their replicas.
Fast model-based clustering of partial records
We compare our approximate algorithm to the corresponding full expectation-maximization (EM) approach that considers the missing values in the incomplete data set and makes a missing at random (MAR) assumption, as well as case deletion and imputation methods.
A practical model-based segmentation approach for improved activation detection in single-subject functional Magnetic Resonance Imaging studies
Finally, the value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS).
Reduced-Rank Tensor-on-Tensor Regression and Tensor-variate Analysis of Variance
Fitting regression models with many multivariate responses and covariates can be challenging, but such responses and covariates sometimes have tensor-variate structure.
An Efficient $k$-modes Algorithm for Clustering Categorical Datasets
Mining clusters from data is an important endeavor in many applications.
CatSIM: A Categorical Image Similarity Metric
We introduce CatSIM, a new similarity metric for binary and multinary two- and three-dimensional images and volumes.
Estimating Basis Functions in Massive Fields under the Spatial Mixed Effects Model
Spatial prediction is commonly achieved under the assumption of a Gaussian random field (GRF) by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values.
Multi-layered characterization of hot stellar systems with confidence
A nonparametric bootstrap procedure was also used to estimate the confidence of each of our group assignments.
A Matrix--free Likelihood Method for Exploratory Factor Analysis of High-dimensional Gaussian Data
This paper proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables.
Classification with the matrix-variate-$t$ distribution
Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures.
TiK-means: $K$-means clustering for skewed groups
The $K$-means algorithm is extended to allow for partitioning of skewed groups.
Visualization of Labeled Mixed-featured Datasets
We develop methodology for visualization of labeled mixed-featured datasets.
Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering
Here, we develop a distribution-free fully-automated syncytial clustering algorithm that can be used with $k$-means and other algorithms.
An efficient $k$-means-type algorithm for clustering datasets with incomplete records
We also provide initialization strategies for our algorithm and methods to estimate the number of groups in the dataset.
Merging $K$-means with hierarchical clustering for identifying general-shaped groups
For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while $K$-means clustering is efficient but designed to identify homogeneous spherically-shaped clusters.
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Approximations in the homogeneous Ising model
The Ising model is important in statistical modeling and inference in many applications, however its normalizing constant, mean number of active vertices and mean spin interaction -- quantities needed in inference -- are computationally intractable.
