BigDataFr recommends: The tensor renaissance in data science
The O’Reilly Data Show Podcast: Anima Anandkumar on tensor decomposition techniques for machine learning.

« After sitting in on UC Irvine Professor Anima Anandkumar’s Strata + Hadoop World 2015 in San Jose presentation, I wrote a post urging the data community to build tensor decomposition libraries for data science. The feedback I’ve gotten from readers has been extremely positive. During the latest episode of the O’Reilly Data Show Podcast, I sat down with Anandkumar to talk about tensor decomposition, machine learning, and the data science program at UC Irvine.

Modeling higher-order relationships

The natural question is: why use tensors when (large) matrices can already be challenging to work with? Proponents are quick to point out that tensors can model more complex relationships. Anandkumar explains:

Tensors are higher order generalizations of matrices. While matrices are two-dimensional arrays consisting of rows and columns, tensors are now multi-dimensional arrays. … For instance, you can picture tensors as a three-dimensional cube. In fact, I have here on my desk a Rubik’s Cube, and sometimes I use it to get a better understanding when I think about tensors.  … One of the biggest use of tensors is for representing higher order relationships. … » » […]

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Ben Lorica, Chief Data Scientist & Director of Content Strategy for Data at O’Reilly Media, Inc
Source: radar.oreilly.com