Fault diagnosis in electronic circuits is an emerging area of research, where fully automated
diagnosis systems are being developed for the investigation of the circuits. Develo** test …

We study the ℓ1-low rank approximation problem, where for a given nxd matrix A and
approximation factor α≤ 1, the goal is to output a rank-k matrix  for which‖ A-Â‖ 1≤ α …

In this era of COVID19, proper diagnosis and treatment of pneumonia are very important.
Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia …

Linear Discriminant Analysis (LDA) is one of the most successful supervised dimensionality
reduction methods and has been widely used in many real-world applications. However, l 2 …

With the boom in Internet and information technology, cyber-attacks are becoming more
frequent and sophisticated, especially Advanced Persistent Threat (APT) attacks. Unlike …

Principal component analysis (PCA) along with its extensions to manifolds and outlier
contaminated data have been indispensable in computer vision and machine learning. In …

In this paper, we develop a novel procedure for low-rank tensor regression, namely
Importance Sketching Low-rank Estimation for Tensors (ISLET). The central idea behind …

The resource-constrained and heterogeneous nature of Internet-of-Things (IoT) devices
coupled with the placement of such devices in publicly accessible venues complicate efforts …

Deep learning models are tools for data analysis suitable for approximating (non-linear)
relationships among variables for the best prediction of an outcome. While these models can …

Tucker decomposition is a standard multi-way generalization of Principal-Component
Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker …