Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Ashley Prater-Bennette"'
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 599-610 (2024)
Leveraging information across diverse modalities is known to enhance performance on multimodal segmentation tasks. However, effectively fusing information from different modalities remains challenging due to the unique characteristics of each modalit
Externí odkaz:
https://doaj.org/article/1f4d4adf362248788393e48daa7bcbc4
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 8 (2022)
The synchrosqueezing transform (SST) and its variants have been developed recently as an alternative to the empirical mode decomposition scheme to model a non-stationary signal as a superposition of amplitude- and frequency-modulated Fourier-like osc
Externí odkaz:
https://doaj.org/article/771dbb31e6f84cb6831dca22da49dc62
Publikováno v:
IEEE Access, Vol 7, Pp 178454-178465 (2019)
Tucker decomposition is a standard multi-way generalization of Principal-Component Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker decomposition has been shown to be sensitive against faulty data, due to its L2-norm-bas
Externí odkaz:
https://doaj.org/article/0f237e193f784f7eb3ba7c81adfb6e21
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 15:587-602
Tucker decomposition is a standard method for processing multi-way (tensor) measurements and finds many applications in machine learning and data mining, among other fields. When tensor measurements arrive in a streaming fashion or are too many to jo
Autor:
Yimin D. Zhang, Ashley Prater-Bennette
Publikováno v:
2021 55th Asilomar Conference on Signals, Systems, and Computers.
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
Publikováno v:
2021 29th European Signal Processing Conference (EUSIPCO).
Autor:
Elizabeth Hale, Ashley Prater-Bennette
Publikováno v:
Big Data III: Learning, Analytics, and Applications.
Structured multidimensional data is often expressed in a tensor format. However, due to the large number of terms, it can be difficult to process, store, interpret, or extract patterns from data in a raw tensor format. To alleviate this, various type
Autor:
Jing Yang, Ashley Prater-Bennette
Publikováno v:
Big Data III: Learning, Analytics, and Applications.
In this work, we consider compressed sensing based pooled testing, where k out of n items are defective (with a non-zero state). Each time, a subset of items are mixed together, and a real-valued quantitative measurement is obtained, where the measur
The log-sum penalty is often adopted as a replacement for the $\ell_0$ pseudo-norm in compressive sensing and low-rank optimization. The hard-thresholding operator, i.e., the proximity operator of the $\ell_0$ penalty, plays an essential role in appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0521adc4553d4818213a35abebceba38