Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Ting, Kai Ming"'
Automatic Modulation Classification (AMC), as a crucial technique in modern non-cooperative communication networks, plays a key role in various civil and military applications. However, existing AMC methods usually are complicated and can work in bat
Externí odkaz:
http://arxiv.org/abs/2410.02750
Recent advancements in industrial anomaly detection have been hindered by the lack of realistic datasets that accurately represent real-world conditions. Existing algorithms are often developed and evaluated using idealized datasets, which deviate si
Externí odkaz:
http://arxiv.org/abs/2410.00713
This paper introduces a new framework for clustering in a distributed network called Distributed Clustering based on Distributional Kernel (K) or KDC that produces the final clusters based on the similarity with respect to the distributions of initia
Externí odkaz:
http://arxiv.org/abs/2409.09418
Anomaly detection is a longstanding and active research area that has many applications in domains such as finance, security, and manufacturing. However, the efficiency and performance of anomaly detection algorithms are challenged by the large-scale
Externí odkaz:
http://arxiv.org/abs/2403.10802
Trajectory clustering enables the discovery of common patterns in trajectory data. Current methods of trajectory clustering rely on a distance measure between two points in order to measure the dissimilarity between two trajectories. The distance mea
Externí odkaz:
http://arxiv.org/abs/2310.05123
Graph anomaly detection has attracted a lot of interest recently. Despite their successes, existing detectors have at least two of the three weaknesses: (a) high computational cost which limits them to small-scale networks only; (b) existing treatmen
Externí odkaz:
http://arxiv.org/abs/2301.06794
Existing measures and representations for trajectories have two longstanding fundamental shortcomings, i.e., they are computationally expensive and they can not guarantee the `uniqueness' property of a distance function: dist(X,Y) = 0 if and only if
Externí odkaz:
http://arxiv.org/abs/2301.00393
Publikováno v:
Journal of Artificial Intelligence Research, 2024, 79: 273-306
Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer fr
Externí odkaz:
http://arxiv.org/abs/2212.14630
Publikováno v:
In Artificial Intelligence November 2024 336
The curse of dimensionality has been studied in different aspects. However, breaking the curse has been elusive. We show for the first time that it is possible to break the curse using the recently introduced Isolation Kernel. We show that only Isola
Externí odkaz:
http://arxiv.org/abs/2109.14198