SeNMFk-SPLIT

Eren, Maksim E.; Solovyev, Nick; Bhattarai, Manish; Rasmussen, Kim Ø.; Nicholas, Charles; Alexandrov, Boian S.

doi:10.1145/3558100.3563844

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…Our decompositions are enhanced by incorporating the semantic structure of the text with the ability to estimate the number of topics, which enables a coherent separation of the latent topics and accurate document clustering. This method is supported by the findings in [2], [3]. Building on [2], [3], we expand our approach through hierarchical decomposition, as well as incorporating semi-supervised labels of arXiv categories in joint factorization to refine our capabilities.…”

Section: A Knowledge Graphsmentioning

confidence: 63%

“…This method is supported by the findings in [2], [3]. Building on [2], [3], we expand our approach through hierarchical decomposition, as well as incorporating semi-supervised labels of arXiv categories in joint factorization to refine our capabilities.…”

Section: A Knowledge Graphsmentioning

confidence: 63%

“…Here we enact joint factorization with the SPLIT method, which allows combination of patterns from separate factorizations. Incorporating the semantic structure of the text with the ability to estimate the number of topics enables a coherent separation of the latent topics and accurate document clustering [2], [3]. We apply the SPLIT method in a hierarchical manner to expand and separate the main topics into sub-topics for a chosen domain, where the extracted sub-semantic structures serve as narrow vocabularies or scientific-jargon seeds for Name Entities Recognition (NER).…”

Section: Introductionmentioning

confidence: 99%

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Barron,

Eren,

Bhattarai

et al. 2024

2024 12th International Symposium on Digital Forensics and Security (ISDFS)

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Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG.

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Section: A Knowledge Graphsmentioning

confidence: 63%

Section: A Knowledge Graphsmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Barron,

Eren,

Bhattarai

et al. 2024

2024 12th International Symposium on Digital Forensics and Security (ISDFS)

Self Cite

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“…Because X is the concatenation of each (H g ) T from the clients, factorization of X is joint factorization, which serves to identify the common global item patterns. Here, we utilize our method SPLIT [34], [35], which excludes patterns that are non-negative linear combinations of other patterns. Since the joint factor matrix X is dense, we apply standard NMF as described in Section II-B to obtain the factor matrices W Global ∈ R m×K + and the transfer matrix,…”

Section: Collaborative Non-negative Matrix Factorization (Cnmf)mentioning

confidence: 99%

One-Shot Federated Group Collaborative Filtering

Eren

Bhattarai

Solovyev

et al. 2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations. However, traditional CF relies on a privacy-invasive collection of user data to build a central recommender model. One-shot federated learning has recently emerged as a method to mitigate the privacy problem while addressing the traditional communication bottleneck of federated learning. In this paper, we present the first one-shot federated CF implementation, named One-FedCF, for groups of users or collaborating organizations. In our solution, the clients first apply local CF in-parallel to build distinct, client-specific recommenders. Then, the privacypreserving local item patterns and biases from each client are shared with the processor to perform joint factorization in order to extract the global item patterns. Extracted patterns are then aggregated to each client to build the local models via information retrieval transfer. In our experiments, we demonstrate our approach with two MovieLens datasets and show results competitive with the state-of-the-art federated recommender systems at a substantial decrease in the number of communications.

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“…We utilized the integrated model selection algorithm previously to decompose the worlds' largest collection of human cancer genomes [9], defining cancer mutational signatures [10], as well as successfully applied to solve real-world problems in various fields [8,[11][12][13][14][15][16][17][18][19].…”

mentioning

confidence: 99%

Distributed Out-of-Memory NMF on CPU/GPU Architectures

Boureima

Bhattarai

Eren

et al. 2023

Preprint

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We propose an efficient distributed out-of-memory implementation of the Non-negative Matrix Factorization (NMF) algorithm for heterogeneous high-performance-computing (HPC) systems. The proposed implementation is based on prior work on NMFk, which can perform automatic model selection and extract latent variables and patterns from data. In this work, we extend NMFk by adding support for dense and sparse matrix operation on multi-node, multi-GPU systems. The resulting algorithm is optimized for out-of-memory (OOM) problems where the memory required to factorize a given matrix is greater than the available GPU memory. Memory complexity is reduced by batching/tiling strategies, and sparse and dense matrix operations are significantly accelerated with GPU cores (or tensor cores when available). Input/Output (I/O) latency associated with batch copies between host and device is hidden using CUDA streams to overlap data transfers and compute asynchronously, and latency associated with collective communications (both intra-node and inter-node) is reduced using optimized NVIDIA Collective Communication Library (NCCL) based communicators. Benchmark results show significant improvement, from 32X to 76x speedup, with the new implementation using GPUs over the CPU-based NMFk. Good weak scaling was demonstrated on up to 4096 multi-GPU cluster nodes with approximately 25,000 GPUs when decomposing a dense 340 Terabyte-size matrix and an 11 Exabyte-size sparse matrix of density 10^{-6}.

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SeNMFk-SPLIT

Cited by 7 publications

References 12 publications

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

One-Shot Federated Group Collaborative Filtering

Distributed Out-of-Memory NMF on CPU/GPU Architectures

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