How Long to Read Tensor Networks for Dimensionality Reduction and Large-scale Optimization

How Long Does it Take to Read Tensor Networks for Dimensionality Reduction and Large-scale Optimization?

It takes the average reader 3 hours to read Tensor Networks for Dimensionality Reduction and Large-scale Optimization by Andrzej Cichocki

Assuming a reading speed of 250 words per minute. Learn more

Description

Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of large-scale, multi-modal and multi-relational datasets. Given that such data are often efficiently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review low-rank tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization problems. Our particular emphasis is on elucidating that, by virtue of the underlying low-rank approximations, tensor networks have the ability to alleviate the curse of dimensionality in a number of applied areas. In Part 1 of this monograph we provide innovative solutions to low-rank tensor network decompositions and easy to interpret graphical representations of the mathematical operations on tensor networks. Such a conceptual insight allows for seamless migration of ideas from the flat-view matrices to tensor network operations and vice versa, and provides a platform for further developments, practical applications, and non-Euclidean extensions. It also permits the introduction of various tensor network operations without an explicit notion of mathematical expressions, which may be beneficial for many research communities that do not directly rely on multilinear algebra. Our focus is on the Tucker and tensor train (TT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide linearly or even super-linearly (e.g., logarithmically) scalable solutions, as illustrated in detail in Part 2 of this monograph.

How long is Tensor Networks for Dimensionality Reduction and Large-scale Optimization?

Tensor Networks for Dimensionality Reduction and Large-scale Optimization by Andrzej Cichocki is 180 pages long, and a total of 45,000 words.

This makes it 61% the length of the average book. It also has 55% more words than the average book.

How Long Does it Take to Read Tensor Networks for Dimensionality Reduction and Large-scale Optimization Aloud?

The average oral reading speed is 183 words per minute. This means it takes 4 hours and 5 minutes to read Tensor Networks for Dimensionality Reduction and Large-scale Optimization aloud.

What Reading Level is Tensor Networks for Dimensionality Reduction and Large-scale Optimization?

Tensor Networks for Dimensionality Reduction and Large-scale Optimization is suitable for students ages 10 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy Tensor Networks for Dimensionality Reduction and Large-scale Optimization?

Tensor Networks for Dimensionality Reduction and Large-scale Optimization by Andrzej Cichocki is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.

To buy Tensor Networks for Dimensionality Reduction and Large-scale Optimization by Andrzej Cichocki on Amazon click the button below.