Search Results for author: Haesun Park

Found 16 papers, 4 papers with code

Skew-Symmetric Adjacency Matrices for Clustering Directed Graphs

no code implementations2 Mar 2022 Koby Hayashi, Sinan G. Aksoy, Haesun Park

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.

Clustering Graph Clustering

SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors

no code implementations8 Oct 2020 Ardavan Afshar, Kejing Yin, Sherry Yan, Cheng Qian, Joyce C. Ho, Haesun Park, Jimeng Sun

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

no code implementations29 Jun 2020 Koby Hayashi, Sinan G. Aksoy, Cheong Hee Park, Haesun Park

We propose a flexible framework for clustering hypergraph-structured data based on recently proposed random walks utilizing edge-dependent vertex weights.


Hybrid Clustering based on Content and Connection Structure using Joint Nonnegative Matrix Factorization

no code implementations28 Mar 2017 Rundong Du, Barry Drake, Haesun Park

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.

Clustering Graph Clustering +1

Outlier Detection for Text Data : An Extended Version

1 code implementation5 Jan 2017 Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park

In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data.

Outlier Detection

PixelSNE: Visualizing Fast with Just Enough Precision via Pixel-Aligned Stochastic Neighbor Embedding

1 code implementation8 Nov 2016 Minjeong Kim, Minsuk Choi, Sunwoong Lee, Jian Tang, Haesun Park, Jaegul Choo

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

1 code implementation28 Sep 2016 Ramakrishnan Kannan, Grey Ballard, Haesun Park

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.

Community Detection

Fast Clustering and Topic Modeling Based on Rank-2 Nonnegative Matrix Factorization

no code implementations3 Sep 2015 Da Kuang, Barry Drake, Haesun Park

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

no code implementations14 Sep 2013 Nicolas Gillis, Da Kuang, Haesun Park

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.

Clustering Vocal Bursts Valence Prediction

Learning the Dependency Structure of Latent Factors

no code implementations NeurIPS 2012 Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park

In this paper, we study latent factor models with the dependency structure in the latent space.

Symmetric Nonnegative Matrix Factorization for Graph Clustering

1 code implementation SDM 2012 Da Kuang, Chris Ding, Haesun Park

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).

Clustering Graph Clustering +1

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