The integration of data and knowledge from several sources is known as data fusion. This
paper summarizes the state of the data fusion field and describes the most relevant studies …

With the rapid development of science and technology, the world is becoming increasingly
connected. The following dire need for understanding both the relationships amongst …

This book provides an introduction to the mathematical and algorithmic foundations of data
science, including machine learning, high-dimensional geometry, and analysis of large …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

We give a tutorial and overview of the field of unsupervised learning from the perspective of
statistical modeling. Unsupervised learning can be motivated from information theoretic and …

When clustering a dataset, the right number k of clusters to use is often not obvious, and
choosing k automatically is a hard algorithmic problem. In this paper we present an …

We describe a learning-based method for low-level vision problems—estimating scenes
from images. We generate a synthetic world of scenes and their corresponding rendered …

Markov random field models provide a robust and unified framework for early vision
problems such as stereo and image restoration. Inference algorithms based on graph cuts …

Graphical models such as factor graphs allow a unified approach to a number of key topics
in coding and signal processing such as the iterative decoding of turbo codes, LDPC codes …