Zobrazeno 1 - 10
of 45 814
pro vyhledávání: '"Non-Negative Matrix Factorization"'
Topic modeling, or more broadly, dimensionality reduction, techniques provide powerful tools for uncovering patterns in large datasets and are widely applied across various domains. We investigate how Non-negative Matrix Factorization (NMF) can intro
Externí odkaz:
http://arxiv.org/abs/2411.09847
Autor:
Muñoz-Montoro, Antonio J., Revuelta-Sanz, Pablo, Martínez-Muñoz, Damian, Torre-Cruz, Juan, Ranilla, José
Publikováno v:
The Journal of Supercomputing, Volume 79, pages 1571-1591, 2023
In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factoriza
Externí odkaz:
http://arxiv.org/abs/2411.05774
Autor:
Kishikawa, Ryo, Harada, Nanase, Saito, Toshiki, Aalto, Susanne, Colzi, Laura, Gorski, Mark, Henkel, Christian, Mangum, Jeffrey G., Martín, Sergio, Muller, Sebastian, Nishimura, Yuri, Rivilla, Víctor M., Sakamoto, Kazushi, van der Werf, Paul, Viti, Serena
It is essential to examine the physical or chemical properties of molecular gas in starburst galaxies to reveal the underlying mechanisms characterizing starbursts. We used non-negative matrix factorization (NMF) to extract individual molecular or ph
Externí odkaz:
http://arxiv.org/abs/2411.03867
Autor:
Lyu, Wenlong, Jia, Yuheng
Symmetric nonnegative matrix factorization (SymNMF) is a powerful tool for clustering, which typically uses the $k$-nearest neighbor ($k$-NN) method to construct similarity matrix. However, $k$-NN may mislead clustering since the neighbors may belong
Externí odkaz:
http://arxiv.org/abs/2412.04082
Generalized Category Discovery (GCD) aims to classify both base and novel images using labeled base data. However, current approaches inadequately address the intrinsic optimization of the co-occurrence matrix $\bar{A}$ based on cosine similarity, fa
Externí odkaz:
http://arxiv.org/abs/2410.21807
Determining the appropriate rank in Non-negative Matrix Factorization (NMF) is a critical challenge that often requires extensive parameter tuning and domain-specific knowledge. Traditional methods for rank determination focus on identifying a single
Externí odkaz:
http://arxiv.org/abs/2410.14838
Autor:
Guo, Youdong, Holy, Timothy E.
Non-negative matrix factorization (NMF) is an important tool in signal processing and widely used to separate mixed sources into their components. However, NMF is NP-hard and thus may fail to discover the ideal factorization; moreover, the number of
Externí odkaz:
http://arxiv.org/abs/2408.08260
Autor:
Vu, Minh, Nebgen, Ben, Skau, Erik, Zollicoffer, Geigh, Castorena, Juan, Rasmussen, Kim, Alexandrov, Boian, Bhattarai, Manish
As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its resilience to such attacks is Non-negative Matrix Factor
Externí odkaz:
http://arxiv.org/abs/2408.03909
Publikováno v:
Proceedings on Engineering Sciences, Vol 6, Pp 1889-1896 (2024)
Technologies like self-driving cars and cleaning robots are emerging as mainstream technologies. These technologies make use of cognitive recognition. Non-negative matrix factorization (NMF) is one such technique that is popularly used for computer v
Externí odkaz:
https://doaj.org/article/fabc3e7a6c024e82adb7b0ccdaf50d71
