Chebyshev's approximation algorithms and applications

Nonnegative Matrix Approximation: Algorithms and Applications

Low dimensional data representations are crucial to numerous applications in machine learning, statistics, and signal processing. Nonnegative matrix approximation (NNMA) is a method for dimensionality reduction that respects the nonnegativity of the input data while constructing a low-dimensional approximation. NNMA has been used in a multitude of applications, though without commensurate theor...

On Approximation Algorithms for Data Mining Applications

We aim to present current trends in the theoretical computer science research on topics which have applications in data mining. We briefly describe data mining tasks in various application contexts. We give an overview of some of the questions and algorithmic issues that are of concern when mining huge amounts of data that do not fit in main memory.

Low Rank Approximation - Algorithms, Implementation, Applications

If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, low rank approximation algorithms implementation applications always be...

Design Algorithms for Doppler MTI and Filters (Using Chebyshev Approximation)

Design Algorithms for Doppler MTI and Filters (Using Chebyshev Approximation)