Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- File Type
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle
Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle
SDSU Data Science Symposium
Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph …
Temporal Tensor Factorization For Multidimensional Forecasting, Jackson Cates, Karissa Scipke, Randy Hoover, Kyle Caudle
Temporal Tensor Factorization For Multidimensional Forecasting, Jackson Cates, Karissa Scipke, Randy Hoover, Kyle Caudle
SDSU Data Science Symposium
In the era of big data, there is a need for forecasting high-dimensional time series that might be incomplete, sparse, and/or nonstationary. The current research aims to solve this problem for two-dimensional data through a combination of temporal matrix factorization (TMF) and low-rank tensor factorization. From this method, we propose an expansion of TMF to two-dimensional data: temporal tensor factorization (TTF). The current research aims to interpolate missing values via low-rank tensor factorization, which produces a latent space of the original multilinear time series. We then can perform forecasting in the latent space. We present experimental results of the proposed …
Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar
Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar
SDSU Data Science Symposium
Data obtained from social media microblogging websites such as Twitter provide the unique ability to collect and analyze conversations of the public in order to gain perspective on the thoughts and feelings of the general public. Sentiment and volume analysis techniques were applied to the dataset in order to gain an understanding of the amount and level of sentiment associated with certain disaster-related tweets, including a topical analysis of specific terms. This study showed that disaster-type events such as a hurricane can cause some strong negative sentiment in the period of time directly preceding the event, but ultimately returns quickly …
Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.
Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.
SDSU Data Science Symposium
Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component …