We introduce a novel profile-based patient clustering model designed for clinical data in healthcare.
Cut-based directed graph (digraph) clustering often focuses on finding dense within-cluster or sparse between-cluster connections, similar to cut-based undirected graph clustering methods.
In particular, we define the N-th order tensor Wasserstein loss for the widely used tensor CP factorization and derive the optimization algorithm that minimizes it.
We propose a flexible framework for clustering hypergraph-structured data based on recently proposed random walks utilizing edge-dependent vertex weights.
TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor.
Understanding narrative content has become an increasingly popular topic.
We propose two variants, SUSTain_M and SUSTain_T, to handle both matrix and tensor inputs, respectively.
We present a hybrid method for latent information discovery on the data sets containing both text content and connection structure based on constrained low rank approximation.
In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data.
Embedding and visualizing large-scale high-dimensional data in a two-dimensional space is an important problem since such visualization can reveal deep insights out of complex data.
NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks.
The effectiveness of this approach is illustrated on several synthetic and real-world hyperspectral images, and shown to outperform standard clustering techniques such as k-means, spherical k-means and standard NMF.
Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).