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 …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

In this paper, we contend that the objective of representation learning is to compress and
transform the distribution of the data, say sets of tokens, towards a mixture of low …

With the technology development in cyber physical systems and big data, there are huge
potential to apply them to achieve personalization and improve resource efficiency in …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Link prediction is one of the most widely studied problems in the area of complex network
analysis, in which machine learning techniques can be applied to deal with it. The biggest …

In the last few decades, Structure from Motion (SfM) and visual Simultaneous Localization
and Mapping (visual SLAM) techniques have gained significant interest from both the …

This paper studies the subspace clustering problem. Given some data points approximately
drawn from a union of subspaces, the goal is to group these data points into their underlying …