This updated compendium provides the linear algebra background necessary to understand and develop linear algebra applications in data mining and machine learning.
Basic knowledge and advanced new topics (spectral theory, singular values, decomposition techniques for matrices, tensors and multidimensional arrays) are presented together with several applications of linear algebra (k-means clustering, biplots, least square approximations, dimensionality reduction techniques, tensors and multidimensional arrays).
The useful reference text includes more than 600 exercises and supplements, many with completed solutions and MATLAB applications.
The volume benefits professionals, academics, researchers and graduate students in the fields of pattern recognition/image analysis, AI, machine learning and databases.
Contents:
- Preface
- About the Author
- Preliminaries
- Linear Spaces
- Matrices
- MATLAB Environment
- Determinants
- Norms and Inner Products
- Eigenvalues
- Similarity and Spectra
- Singular Values
- The k-Means Clustering
- Data Sample Matrices
- Least Squares Approximations and Data Mining
- Dimensionality Reduction Techniques
- Tensors and Exterior Algebras
- Multidimensional Array and Tensors
- Bibliography
- Index
Readership: Researchers, professionals, academics and graduate students in pattern recognition/image analysis, AI, machine learning and databases.
