Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Arin Chaudhuri"'
Publikováno v:
2023 Annual Reliability and Maintainability Symposium (RAMS).
Publikováno v:
IEEE Transactions on Multimedia. 22:59-68
Robust principal component analysis (PCA) is a key technique for dynamical high-dimensional data analysis, including background subtraction for surveillance video. Typically, robust PCA requires all observations to be stored in memory before processi
Publikováno v:
Knowledge Science, Engineering and Management ISBN: 9783030821524
KSEM
KSEM
Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases with high dimensions in data science. A successful dimension reduction and visualization method s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1803b65774c0e503ca0b5081af1a7aaf
https://doi.org/10.1007/978-3-030-82153-1_30
https://doi.org/10.1007/978-3-030-82153-1_30
Publikováno v:
2020 Annual Reliability and Maintainability Symposium (RAMS).
Sensor data analysis plays a key role in health assessment of critical equipment. Such data are multivariate and exhibit nonlinear relationships. This paper describes how one can exploit nonlinear dimension reduction techniques, such as the t-distrib
Publikováno v:
IEEE BigData
In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anoma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4f170b464dc23b5006bba528edabeed
Autor:
Sergiy Peredriy, Hansi Jiang, Seunghyun Kong, Haoyu Wang, Carol Sadek, Arin Chaudhuri, Wenhao Hu, Deovrat Kakde, Yuewei Liao
Publikováno v:
Pattern Recognition. 111:107662
Support vector data description (SVDD) is a popular anomaly detection technique. The computation of the SVDD classifier requires a kernel function, for which the Gaussian kernel is a common choice. The Gaussian kernel has a bandwidth parameter, and i
Autor:
Wenhao Hu, Arin Chaudhuri
Classical dependence measures such as Pearson correlation, Spearman’s ρ , and Kendall’s τ can detect only monotonic or linear dependence. To overcome these limitations, Szekely et al. proposed distance covariance and its derived correlation. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72b43d7dbebffde7a5e5c57cf167b9d9
http://arxiv.org/abs/1810.11332
http://arxiv.org/abs/1810.11332
This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular machine learning technique for single-class classification, but selecting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5057e289f83307993365954389953b54
Publikováno v:
IEEE BigData
Support vector data description (SVDD) provides a useful approach, with various practical applications, for constructing a description of multivariate data for single-class classification and outlier detection. The Gaussian kernel that is used in SVD
Publikováno v:
ICDM Workshops
Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of