By Haiping Lu,Konstantinos N. Plataniotis,Anastasios Venetsanopoulos

Due to advances in sensor, garage, and networking applied sciences, info is being generated each day at an ever-increasing speed in a variety of functions, together with cloud computing, cellular net, and scientific imaging. this huge multidimensional information calls for extra effective dimensionality aid schemes than the conventional innovations. Addressing this desire, multilinear subspace studying (MSL) reduces the dimensionality of huge facts at once from its traditional multidimensional illustration, a tensor.


Multilinear Subspace studying: Dimensionality aid of Multidimensional Data supplies a finished creation to either theoretical and sensible points of MSL for the dimensionality relief of multidimensional information in accordance with tensors. It covers the basics, algorithms, and functions of MSL.



Emphasizing crucial ideas and system-level views, the authors supply a origin for fixing a lot of today’s finest and hard difficulties in immense multidimensional facts processing. They hint the heritage of MSL, element contemporary advances, and discover destiny advancements and rising applications.



The publication follows a unifying MSL framework formula to systematically derive consultant MSL algorithms. It describes numerous functions of the algorithms, besides their pseudocode. Implementation tips aid practitioners in extra improvement, evaluate, and alertness. The e-book additionally presents researchers with priceless theoretical info on sizeable multidimensional info in laptop studying and trend reputation. MATLAB® resource code, facts, and different fabrics can be found at www.comp.hkbu.edu.hk/~haiping/MSL.html

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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition) by Haiping Lu,Konstantinos N. Plataniotis,Anastasios Venetsanopoulos


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