GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations
T Song, C Broadbent, R Kuang - Nature communications, 2023 - nature.com
Spatially-resolved RNA profiling has now been widely used to understand cells' structural
organizations and functional roles in tissues, yet it is challenging to reconstruct the whole …
organizations and functional roles in tissues, yet it is challenging to reconstruct the whole …
Topic modeling methods for text data analysis: a review
A Helan, ZN Sultani - AIP Conference Proceedings, 2023 - pubs.aip.org
Topic modeling is the task of identifying topics in a corpus of documents. This is useful for
search engines, customer service automation, and any other situation where document …
search engines, customer service automation, and any other situation where document …
Neural nonnegative matrix factorization for hierarchical multilayer topic modeling
T Will, R Zhang, E Sadovnik, M Gao, J Vendrow… - arXiv preprint arXiv …, 2023 - arxiv.org
We introduce a new method based on nonnegative matrix factorization, Neural NMF, for
detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a …
detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a …
COVID-19 literature topic-based search via hierarchical NMF
A dataset of COVID-19-related scientific literature is compiled, combining the articles from
several online libraries and selecting those with open access and full text available. Then …
several online libraries and selecting those with open access and full text available. Then …
Optimal Number of Topics in Topic Modeling Using Deep Auto Encoder Graph Regularized Non-Negative Matrix Factorization Algorithm
P Kherwa, J Arora - Journal of Systems Science and Systems Engineering, 2024 - Springer
Topic modeling stands as a well-explored and foundational challenge in the text mining
domain. Traditional topic schemes based on word co-occurrences, aim to expose the latent …
domain. Traditional topic schemes based on word co-occurrences, aim to expose the latent …
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. Dynamic topic modeling has been widely used in social and …
the explosive growth of data. Dynamic topic modeling has been widely used in social and …
A generalized hierarchical nonnegative tensor decomposition
Nonnegative matrix factorization (NMF) has found many applications including topic
modeling and document analysis. Hierarchical NMF (HNMF) variants are able to learn topics …
modeling and document analysis. Hierarchical NMF (HNMF) variants are able to learn topics …
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. Datasets with hierarchical structure arise in a …
detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a …
Multi-scale Hybridized Topic Modeling: A Pipeline for analyzing unstructured text datasets via Topic Modeling
We propose a multi-scale hybridized topic modeling method to find hidden topics from
transcribed interviews more accurately and efficiently than traditional topic modeling …
transcribed interviews more accurately and efficiently than traditional topic modeling …
Topic Modeling Approaches—A Comparative Analysis
D Lakshminarayana Reddy, C Shoba Bindu - International Conference on …, 2022 - Springer
Valuable information from a corpus for a specific purpose can be obtained by finding,
extracting, and processing the text through text mining. A corpus is a group of documents …
extracting, and processing the text through text mining. A corpus is a group of documents …