This paper uses network packet capture data to demonstrate how Robust Principal Component Analysis (RPCA) can be used in a new way to detect anomalies which serve as cyber-network attack indicators. The approach requires only a few parameters to be learned using partitioned training data and shows promise of ameliorating the need for an exhaustive set of examples of different types of network a...

Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e.,...

Moving object detection is a key step in video surveillance system. Recently, Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background when the camera is fixed. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving objects constitute the correlated sparse outliers. In this pap...

The network traffic matrix is widely used in network operation and management. It is therefore of crucial importance to analyze the components and the structure of the network traffic matrix, for which several mathematical approaches such as Principal Component Analysis (PCA) were proposed. In this paper, we first argue that PCA performs poorly for analyzing traffic matrix that is polluted by l...

Calcium imaging is an essential tool to study the activity of neuronal populations. However, high level background fluorescence in images hinders accurate identification neurons and extraction activities. While robust principal component analysis (RPCA) a promising method that can decompose foreground such images, its computational complexity memory requirement are prohibitively process large-s...

The target detection ability of an infrared small (ISTD) system is advantageous in many applications. highly varied nature the background image and characteristics make process extremely difficult. To address this issue, study proposes patch model using non-convex (IPNCWNNM) weighted nuclear norm minimization (WNNM) robust principal component analysis (RPCA). As observed most advanced methods i...

Robust Principal Components Analysis (RPCA) gives a suitable framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving objects constitute the correlated sparse outliers. RPCA problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm an...