Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …

High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …

Profiting from large-scale training datasets, advances in neural architecture design and
efficient inference, joint embeddings have become the dominant approach for tackling cross …

Computational Intelligence comprises concepts, paradigms, algorithms, and
implementations of systems that are supposed to exhibit intelligent behavior in complex …

Different aspects of the curse of dimensionality are known to present serious challenges to
various machine-learning methods and tasks. This paper explores a new aspect of the …

Outlier detection in high-dimensional data presents various challenges resulting from the
“curse of dimensionality.” A prevailing view is that distance concentration, ie, the tendency of …

This paper represents another step in overcoming a drawback of K-Means, its lack of
defense against noisy features, using feature weights in the criterion. The Weighted K …

In this paper, a random frequency diverse array-based directional modulation with artificial
noise (RFDA-DM-AN) scheme is proposed to enhance physical layer security of wireless …

The performance of similarity measures for search, indexing, and data mining applications
tends to degrade rapidly as the dimensionality of the data increases. The effects of the so …

Origin-destination (OD) movement data describe moves or trips between spatial locations by
specifying the origins, destinations, start, and end times, but not the routes travelled. For …