Statistical Spectral Analysis—A Nonprobabilistic Theory

Statistical Foundation of Spectral Graph Theory

Spectral graph theory is undoubtedly the most favored graph data analysis technique, both in theory and practice. It has emerged as a versatile tool for a wide variety of applications including data mining, web search, quantum computing, computer vision, image segmentation, and among others. However, the way in which spectral graph theory is currently taught and practiced is rather mechanical, ...

A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory

A functional defined on the class of generalized characteristic functions (fuzzy sets), called "entropy", is introduced using no probabilistic concepts in order to obtain a global measure of the indefiniteness connected with the situations described by fuzzy sets. This "entropy" may be regarded as a measure of a quantity of information which is not necessarily related to random experiments. Som...

Spectral Theory

Spectral theory has for its origin the classical theory of finite matrices and, in a sense, includes all theories which may be specialized to some particular phase of that classical theory. Such a meaning for the term spectral theory is perhaps too broad, for its acceptance would force one, as is becoming increasingly evident, to admit that spectral theory embraces not a small part of such fiel...

Spectral Theory

Spectral Theory

In many applications it is important to understand the spectral properties of a linear operator T : X → X, where X is some vector space over IR or I C. In the finite dimensional (complex) case linear operators may be characterised as matrices and the Jordan normal form theorem applies, providing a basis of generalised eigenvectors. If, in addition, T is normal (i.e. T and T ∗ commute) with resp...