Structured multidimensional data is often expressed in a tensor format. However, due to the large number of terms, it can be difficult to process, store, interpret, or extract patterns from data in a raw tensor format. To alleviate this, various types of tensor decompositions have been developed to reduce the number of terms used to represent multidimensional data, as well as to reveal underlying structure and relationships among the variables. This article will explore variations on two types of tensor decompositions, the CANDECOMP/PARAFAC and the Tucker decompositions, and perform baseline comparisons of their associated algorithms on common datasets with similar choices of parameters. We perform numerical experiments on synthetic and real-world data to directly compare multiple algorithms to approximate the two tensor decomposition types. We also present results comparing the two decomposition models and their algorithms for image denoising and completion examples.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.