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Found 13 papers, 6 papers with code
Neural Nonnegative Matrix Factorization for Hierarchical Multilayer Topic Modeling
We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data.
Automatic Infectious Disease Classification Analysis with Concept Discovery
Automatic infectious disease classification from images can facilitate needed medical diagnoses.
An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure
In this paper, we propose a new method for determining shared features of and measuring the distance between data sets or point clouds.
Nonbacktracking spectral clustering of nonuniform hypergraphs
Spectral methods offer a tractable, global framework for clustering in graphs via eigenvector computations on graph matrices.
Semi-supervised Nonnegative Matrix Factorization for Document Classification
We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators.
A Generalized Hierarchical Nonnegative Tensor Decomposition
Hierarchical NTF (HNTF) methods have been proposed, however these methods do not naturally generalize their matrix-based counterparts.
Neural Nonnegative CP Decomposition for Hierarchical Tensor Analysis
We propose a new hierarchical nonnegative CANDECOMP/PARAFAC (CP) decomposition (hierarchical NCPD) model and a training method, Neural NCPD, for performing hierarchical topic modeling on multi-modal tensor data.
On a Guided Nonnegative Matrix Factorization
Fully unsupervised topic models have found fantastic success in document clustering and classification.
On Application of Block Kaczmarz Methods in Matrix Factorization
Matrix factorization techniques compute low-rank product approximations of high dimensional data matrices and as a result, are often employed in recommender systems and collaborative filtering applications.
Semi-supervised NMF Models for Topic Modeling in Learning Tasks
We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific distributions of uncertainty.
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Feature Selection on Lyme Disease Patient Survey Data
We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC.
On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data.
Data-driven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing
Machine learning algorithms typically rely on optimization subroutines and are well-known to provide very effective outcomes for many types of problems.
