Matrix Information Geometry
Frank Nielsen · Rajendra Bhatia
ສ.ຫ. 2012 · Springer Science & Business Media
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This book presents advances in matrix and tensor data processing in the domain of signal, image and information processing. The theoretical mathematical approaches are discusses in the context of potential applications in sensor and cognitive systems engineering.
The topics and application include Information Geometry, Differential Geometry of structured Matrix, Positive Definite Matrix, Covariance Matrix, Sensors (Electromagnetic Fields, Acoustic sensors) and Applications in Cognitive systems, in particular Data Mining.
The topics and application include Information Geometry, Differential Geometry of structured Matrix, Positive Definite Matrix, Covariance Matrix, Sensors (Electromagnetic Fields, Acoustic sensors) and Applications in Cognitive systems, in particular Data Mining.
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ກ່ຽວກັບຜູ້ຂຽນ
Frank Nielsen is Senior Researcher at Sony Computer Science Laboratories Inc, Tokyo, Japan.
He is conducting research on information sciences for data analytics based on the framework of computational information geometry with applications in visual computing.
He has authored more than hundred research papers and two books including "Visual Computing: Geometry, Graphics, and Vision".
Rajendra Bhatia is Professor of Mathematics at the Indian Statistical Institute in New Delhi, India.
He is the author of five books including "Matrix Analysis" and "Positive Definite Matrices"..
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