Search Results for author:
Found 16 papers, 4 papers with code
Patient Clustering via Integrated Profiling of Clinical and Digital Data
We introduce a novel profile-based patient clustering model designed for clinical data in healthcare.
Skew-Symmetric Adjacency Matrices for Clustering Directed Graphs
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.
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
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.
Hypergraph Random Walks, Laplacians, and Clustering
We propose a flexible framework for clustering hypergraph-structured data based on recently proposed random walks utilizing edge-dependent vertex weights.
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor.
TopicSifter: Interactive Search Space Reduction Through Targeted Topic Modeling
Topic modeling is commonly used to analyze and understand large document collections.
Understanding Actors and Evaluating Personae with Gaussian Embeddings
Understanding narrative content has become an increasingly popular topic.
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping
We propose two variants, SUSTain_M and SUSTain_T, to handle both matrix and tensor inputs, respectively.
Hybrid Clustering based on Content and Connection Structure using Joint Nonnegative Matrix Factorization
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.
Outlier Detection for Text Data : An Extended Version
In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data.
PixelSNE: Visualizing Fast with Just Enough Precision via Pixel-Aligned Stochastic Neighbor Embedding
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.
MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization
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.
Fast Clustering and Topic Modeling Based on Rank-2 Nonnegative Matrix Factorization
In this paper, we propose a fast method for hierarchical clustering and topic modeling called HierNMF2.
Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization
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.
Learning the Dependency Structure of Latent Factors
In this paper, we study latent factor models with the dependency structure in the latent space.
Symmetric Nonnegative Matrix Factorization for Graph Clustering
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).
