In Cloud Ethics Louise Amoore examines how machine learning algorithms are transforming
the ethics and politics of contemporary society. Conceptualizing algorithms as ethicopolitical …

A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

Automatic Face Recognition (FR) presents a challenging task in the field of pattern
recognition and despite the huge research in the past several decades; it still remains an …

Sparse coding-based anomaly detection has shown promising performance, of which the
keys are feature learning, sparse representation, and dictionary learning. In this paper, we …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

Existing subspace clustering methods typically employ shallow models to estimate
underlying subspaces of unlabeled data points and cluster them into corresponding groups …

Different imaging solutions have been proposed over the last few decades, aimed at three-
dimensional (3D) space reconstruction and obstacle detection, either based on stereo-vision …

In pattern recognition and computer vision, one is often faced with scenarios where the
training data used to learn a model have different distribution from the data on which the …

A denoising algorithm seeks to remove noise, errors, or perturbations from a signal.
Extensive research has been devoted to this arena over the last several decades, and as a …

The “German Traffic Sign Recognition Benchmark” is a multi-category classification
competition held at IJCNN 2011. Automatic recognition of traffic signs is required in …