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I've noticed lately that a lot of people are developing tensor equivalents of many methods (tensor factorization, tensor kernels, tensors for topic modeling, etc) I'm wondering, why is the world suddenly fascinated with tensors? Are there recent papers/ standard results that are particularly surprising, that brought about this? Is it computationally a lot cheaper than previously suspected?

I'm not being glib, I sincerely am interested, and if there are any pointers to papers about this, I'd love to read them.

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It seems like the only retaining feature that "big data tensors" share with the usual mathematical definition is that they are multidimensional arrays. So I'd say that big data tensors are a marketable way of saying "multidimensional array," because I highly doubt that machine learning people will care about either the symmetries or transformation laws that the usual tensors of mathematics and physics enjoy, especially their usefulness in forming coordinate free equations. – Alex R. Feb 23 at 19:00
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@AlexR. without invariance to transformations there are no tensors – Aksakal Feb 23 at 21:43
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@Aksakal I'm certainly somewhat familiar with the use of tensors in physics. My point would be that the symmetries in physics tensors come from symmetry of the physics, not something essential in the defn of tensor. – aginensky Feb 24 at 19:54
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@aginensky If a tensor were nothing more than a multidimensional array, then why do the definitions of tensors found in math textbooks sound so complicated? From Wikipedia: "The numbers in the multidimensional array are known as the scalar components of the tensor... Just as the components of a vector change when we change the basis of the vector space, the components of a tensor also change under such a transformation. Each tensor comes equipped with a transformation law that details how the components of the tensor respond to a change of basis." In math, a tensor is not just an array. – littleO Feb 25 at 0:55
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Just some general thoughts on this discussion: I think that, as with vectors and matrices, the actual application often becomes a much-simplified instantiation of much richer theory. I am reading this paper in more depth: epubs.siam.org/doi/abs/10.1137/07070111X?journalCode=siread and one thing that is really impressing me is that the "representational" tools for matrices (eigenvalue and singular value decompositions) have interesting generalizations in higher orders. I'm sure there are many more beautiful properties as well, beyond just a nice container for more indices. :) – whyyes Feb 25 at 15:40

Tensors often offer more natural representations of data, e.g., consider video, which consists of obviously correlated images over time. You can turn this into a matrix, but it's just not natural or intuitive (what does a factorization of some matrix-representation of video mean?).

Tensors are trending for several reasons:

  • our understanding of multilinear algebra is improving rapidly, specifically in various types of factorizations, which in turn helps us to identify new potential applications (e.g., multiway component analysis)
  • software tools are emerging (e.g., Tensorlab) and are being welcomed
  • Big Data applications can often be solved using tensors, for example recommender systems, and Big Data itself is hot
  • increases in computational power, as some tensor operations can be hefty (this is also one of the major reasons why deep learning is so popular now)
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On the computational power part: I think the most important is that linear algebra can be very fast on GPUs, and lately they have gotten bigger and faster memories, that is the biggest limitation when processing large data. – Davidmh Feb 23 at 12:17
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Marc Claesen's answer is a good one. David Dunson, Distinguished Professor of Statistics at Duke, has been one of the key exponents of tensor-based approaches to modeling as in this presentation, Bayesian Tensor Regression. icerm.brown.edu/materials/Slides/sp-f12-w1/… – DJohnson Feb 23 at 14:36
    
As mentioned by David, Tensor algorithms often lend themselves well to parallelism, which hardware (such as GPU accelerators) are increasingly getting better at. – Shaktal Feb 23 at 15:15
    
I assumed that the better memory/CPU capabilities were playing a part, but the very recent burst of attention was interesting; I think it must be because of a lot of recent surprising successes with recommender systems, and perhaps also kernels for SVMs, etc. Thanks for the links! great places to start learning about this stuff... – whyyes Feb 24 at 7:20
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If you store a video as a multidimensional array, I don't see how this multidimensional array would have any of the invariance properties a tensor is supposed to have. It doesn't seem like the word "tensor" is appropriate in this example. – littleO Feb 24 at 8:45

I think your question should be matched with an answer that is equally free flowing and open minded as the question itself. So, here they are my two analogies.

First, unless you're a pure mathematician, you were probably taught univariate probabilities and statistics first. For instance, most likely your first OLS example was probably on a model like this: $$y_i=a+bx_i+e_i$$ Most likely, you went through deriving the estimates through actually minimizing the sum of least squares: $$TSS=\sum_i(y_i-\bar a-\bar b x_i)^2$$ Then you write the FOCs for parameters and get the solution: $$\frac{\partial TTS}{\partial \bar a}=0$$

Then later you're told that there's an easier way of doing this with vector (matrix) notation: $$y=Xb+e$$

and the TTS becomes: $$TTS=(y-X\bar b)'(y-X\bar b)$$

The FOCs are: $$2X'(y-X\bar b)=0$$

And the solution is $$\bar b=(X'X)^{-1}X'y$$

If you're good at linear algebra, you'll stick to the second approach once you've learned it, because it's actually easier than writing down all the sums in the first approach, especially once you get into multivariate statistics.

Hence my analogy is that moving to tensors from matrices is similar to moving from vectors to matrices: if you know tensors some things will look easier this way.

Second, where do the tensors come from? I'm not sure about the whole history of this thing, but I learned them in theoretical mechanics. Certainly, we had a course on tensors, but I didn't understand what was the deal with all these fancy ways to swap indices in that math course. It all started to make sense in the context of studying tension forces.

So, in physics they also start with a simple example of pressure defined as force per unit area, hence: $$F=p\cdot dS$$ This means you can calculate the force vector $F$ by multiplying the pressure $p$ (scalar) by the unit of area $dS$ (normal vector). That is when we have only one infinite plane surface. In this case there's just one perpendicular force. A large balloon would be good example.

However, if you're studying tension inside materials, you are dealing with all possible directions and surfaces. In this case you have forces on any given surface pulling or pushing in all directions, not only perpendicular ones. Some surfaces are torn apart by tangential forces "sideways" etc. So, your equation becomes: $$F=P\cdot dS$$ The force is still a vector $F$ and the surface area is still represented by its normal vector $dS$, but $P$ is a tensor now, not a scalar.

Ok, a scalar and a vector are also tensors :)

Another place where tensors show up naturally is covariance or correlation matrices. Just think of this: how to transform once correlation matrix $C_0$ to another one $C_1$? You realize we can't just do it this way: $$C_\theta(i,j)=C_0(i,j)+ \theta(C_1(i,j)-C_0(i,j)),$$ where $\theta\in[0,1]$ because we need to keep all $C_\theta$ positive semi-definite.

So, we'd have to find the path $\delta C_\theta$ such that $C_1=C_0+\int_\theta\delta C_\theta$, where $\delta C_\theta$ is a small disturbance to a matrix. There are many different paths, and we could search for the shortest ones. That's how we get into Riemannian geometry, manifolds, and... tensors.

UPDATE: what's tensor, anyway?

@amoeba and others got into a lively discussion of the meaning of tensor and whether it's the same as an array. So, I thought an example is in order.

Say, we go to a bazaar to buy groceries, and there are two merchant dudes, $d_1$ and $d_2$. We noticed that if we pay $x_1$ dollars to $d_1$ and $x_2$ dollars to $d_2$ then $d_1$ sells us $y_1=2x_1-x_2$ pounds of apples, and $d_2$ sells us $y_2=-0.5x_1+2x_2$ oranges. For instance, if we pay both 1 dollar, i.e. $x_1=x_2=1$, then we must get 1 pound of apples and 1.5 of oranges.

We can express this relation in the form of a matrix $P$:

 2   -1
-0.5  2 

Then the merchants produce this much apples and oranges if we pay them $x$ dollars: $$y=Px$$

This works exactly like a matrix by vector multiplication.

Now, let's say instead of buying the goods from these merchants separately, we declare that there are two spending bundles we utilize. We either pay both 0.71 dollars, or we pay $d_1$ 0.71 dollars and demand 0.71 dollars from $d_2$ back. Like in the initial case, we go to a bazaar and spend $z_1$ on the bundle one and $z_2$ on the bundle 2.

So, let's look at an example where we spend just $z_1=1.42$ on bundle 1. In this case, the first merchant gets $x_1=1$ dollars, and the second merchant gets the same $x_2=1$. Hence, we must get the same amounts of produce like in the example above, aren't we?

Maybe, maybe not. You noticed that $P$ matrix is not diagonal. This indicates that for some reason how much one merchant charges for his produce depends also on how much we paid the other merchant. They must get an idea of how much pay them, maybe through roumors? In this case, if we start buying in bundles they'll know for sure how much we pay each of them, because we declare our bundles to the bazaar. In this case, how do we know that the $P$ matrix should stay the same?

Maybe with full information of our payments on the market the pricing formulae would change too! This will change our matrix $P$, and there's no way to say how exactly.

This is where we enter tensors. Essentially, with tensors we say that the calculations do not change when we start trading in bundles instead of directly with each merchant. That's the constraint, that will impose transformation rules on $P$, which we'll call a tensor.

Particularly we may notice that we have an orthonormal basis $\bar d_1,\bar d_2$, where $d_i$ means a payment of 1 dollar to a merchant $i$ and nothing to the other. We may also notice that the bundles also form an orthonormal basis $\bar d_1',\bar d_2'$, which is also a simple rotation of the first basis by 45 degrees counterclockwise. It's also a PC decomposition of the first basis. hence, we are saying that switching to the bundles is simple a change of coordinates, and it should not change the calculations. Note, that this an outside constraint that we imposed on the model. It didn't come from pure math properties of matrices.

Now, our shopping can be expressed as a vector $x=x_1 \bar d_1+x_2\bar d_2$. The vectors are tensors too, btw. The tensor is interesting: it can be represented as $$P=\sum_{ij}p_{ij}\bar d_i\bar d_j$$, and the groceries as $y=y_1 \bar d_1+y_2 \bar d_2$. With groceries $y_i$ means pound of produce from the merchant $i$, not the dollars paid.

Now, when we changed the coordinates to bundles the tensor equation stays the same: $$y=Pz$$

That's nice, but the payment vectors are now in the different basis: $$z=z_1 \bar d_1'+z_2\bar d_2'$$, while we may keep the produce vectors in the old basis $y=y_1 \bar d_1+y_2 \bar d_2$. The tensor changes too:$$P=\sum_{ij}p_{ij}'\bar d_i'\bar d_j'$$. It's easy to derive how the tensor must be transformed, it's going to be $PA$, where the rotation matrix is defined as $\bar d'=A\bar d$. In our case it's the coefficient of the bundle.

We can work out the formulae for tensor transformation, and they'll yield the same result as in the examples with $x_1=x_2=1$ and $z_1=0.71,z_2=0$.

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I got confused around here: So, let's look at an example where we spend just z1=1.42 on bundle 1. In this case, the first merchant gets x1=1 dollars, and the second merchant gets the same x2=1. Earlier you say that first bundle is that we pay both 0.71 dollars. So spending 1.42 on the first bundle should get 0.71 each and not 1, no? – amoeba Feb 25 at 10:32
    
@ameba, the idea's that a bundle 1 is $\bar d_1/ \sqrt 2+\bar d_2/ \sqrt 2$, so with $\sqrt 2$ bundle 1 you get $\bar d_1+\bar d_2$, i.e. 1\$ each – Aksakal Feb 25 at 14:44

This is not an answer to your question, but an extended comment on the issue that has been raised here in comments by different people, namely: are machine learning "tensors" the same thing as tensors in mathematics?

Now, according to the Cichoki 2014, Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions, and Cichoki et al. 2014, Tensor Decompositions for Signal Processing Applications,

A higher-order tensor can be interpreted as a multiway array, [...]

A tensor can be thought of as a multi-index numerical array, [...]

Tensors (i.e., multi-way arrays) [...]

So called tensors in machine learning

So in machine learning / data processing a tensor appears to be simply defined as a multidimensional numerical array. An example of such a 3D tensor would be $1000$ video frames of $640\times 480$ size. A usual $n\times p$ data matrix is an example of a 2D tensor according to this definition.

This is not how tensors are defined in mathematics and physics!

A tensor can be defined as a multidimensional array obeying certain transformation laws under the change of coordinates (see Wikipedia or the first sentence in MathWorld article). A better but equivalent definition (see Wikipedia) says that a tensor on vector space $V$ is an element of $V\otimes\ldots\otimes V^*$. Note that this means that, when represented as multidimensional arrays, tensors are of size $p\times p$ or $p\times p\times p$ etc., where $p$ is the dimensionality of $V$.

All tensors well-known in physics are like that: inertia tensor in mechanics is $3\times 3$, electromagnetic tensor in special relativity is $4\times 4$, Riemann curvature tensor in general relativity is $4\times 4\times 4\times 4$. Curvature and electromagnetic tensors are actually tensor fields, which are sections of tensor bundles (see e.g. here but it gets technical), but all of that is defined over a vector space $V$.

Of course one can consider a tensor product $V\otimes W$ of an $p$-dimensional $V$ and $q$-dimensional $W$ and choose to call its elements "tensors", but I am not aware of any useful such "tensor" and Wikipedia says that

In principle, one could define a "tensor" simply to be an element of any tensor product. However, the mathematics literature usually reserves the term tensor for an element of a tensor product of a single vector space $V$ and its dual, as above.

One example of a real tensor in statistics would be a covariance matrix. It is $p\times p$ and transforms in a particular way when the coordinate system in the $p$-dimensional feature space $V$ is changed. It is a tensor. But a $n\times p$ data matrix $X$ is not.

But can we at least think of $X$ as a "generalized" tensor in the sense of an element of tensor product $W\otimes V$, where $W$ is $n$-dimensional and $V$ is $p$-dimensional? For concreteness, let rows in $X$ correspond to people (subjects) and columns to some measurements (features). Linear transformations in $V$ correspond to linear transformation of features, and this is done in statistics all the time (think of PCA). But linear transformations in $W$ do not seem to correspond to anything meaningful: what would be a linear combination of people mean? So it does not seem that there is anything gained by considering $X$ as an element of $W\otimes V$.

My conclusion is: machine learning "tensors" are not actually tensors in any meaningful or useful way.

Instead, they are multidimensional generalizations of matrices. Unfortunately, there is no established mathematical term for that.

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I am a pure mathematician, and this is a very good answer. In particular, the example of a covariance matrix is an excellent way to understand the "transformation properties" or "symmetries" that seemed to cause confusion above. If you change coordinates on your $p$-dimensional feature space, the covariance matrix transforms in a particular and possibly surprising way; if you did the more naive transformation on your covariances you would end up with incorrect results. – Tom Church Feb 25 at 4:08
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Thanks, @Tom, I appreciate that you registered on CrossValidated to leave this comment. It has been a long time since I was studying differential geometry so I am happy if somebody confirms what I wrote. It is a pity that there is no established term in mathematics for "multidimensional matrices"; it seems that "tensor" is going to stick in machine learning community as a term for that. How do you think one should rather call it though? The best thing that comes to my mind is $n$-matrices (e.g. $3$-matrix to refer to a video object), somewhat analogously to $n$-categories. – amoeba Feb 25 at 10:25
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@amoeba, in programming the multidemensional matrices are usually called arrays, but some languages such as MATLAB would call them matrices. For instance, in FORTRAN the arrays can have more than 2 dimensions. In languages like C/C++/Java the arrays are one dimensional, but you can have arrays of arrays, making them work like multidimensional arrays too. MATLAB supports 3 or more dimensional arrays in the syntax. – Aksakal Feb 25 at 15:17
    
@amoeba I studied Riemannian geometry in school, and will second this answer as accurate and enjoyable. – Matthew Drury Feb 25 at 16:06
    
@amoeba, on people by features, I see how this could work. Let's say the features $v_j$ are skin, hair and eye colors: $v_1,v_2,v_3$, and people are $w_i$, using your notation. Let's say we are modeling incidents of cancers in people $i$, i.e. $y_i$ is whether a person $i$ got skin cancer or not. So, we can form a basis $e_i$, where $e_i=1$ if it's person $i$, and zero otherwise. So, we can write the tensor equation now: $y=Px$, where $P$ is a tensor. Now, you see where I'm going: we can turn the basis, because it's likely that people with fair skins have more skin cancer. – Aksakal Feb 25 at 16:22

Now I actually agree with most of the content of the other answers. But I'm going to play Devil's advocate on one point. Again, it will be free flowing, so apologies...

Google announced a program called Tensor Flow for deep learning. This made me wonder what was 'tensor' about deep learning, as I couldn't make the connection to the definitions I'd seen.

enter image description here

Deep learning models are all about transformation of elements from one space to another. E.g. if we consider two layers of some network you might write co-ordinate $i$ of a transformed variable $y$ as a nonlinear function of the previous layer, using the fancy summation notation:

$y_i = \sigma(\beta_i^j x_j)$

Now the idea is to chain together a bunch of such transformations in order to arrive at a useful representation of the original co-ordinates. So, for example, after the last transformation of an image a simple logistic regression will produce excellent classification accuracy; whereas on the raw image it would definitely not.

Now, the thing that seems to have been lost from sight is the invariance properties sought in a proper tensor. Particularly when the dimensions of transformed variables may be different from layer to layer. [E.g. some of the stuff I've seen on tensors makes no sense for non square Jacobians - I may be lacking some methods]

What has been retained is the notion of transformations of variables, and that certain representations of a vector may be more useful than others for particular tasks. Analogy being whether it makes more sense to tackle a problem in Cartesian or polar co-ordinates.


EDIT in response to @Aksakal:

The vector can't be perfectly preserved because of the changes in the numbers of coordinates. However, in some sense at least the useful information may be preserved under transformation. For example with PCA we may drop a co-ordinate, so we can't invert the transformation but the dimensionality reduction may be useful nonetheless. If all the successive transformations were invertible, you could map back from the penultimate layer to input space. As it is, I've only seen probabilistic models which enable that (RBMs) by sampling.

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In the context of neural networks I had always assumed tensors were acting just as multidimensional arrays. Can you elaborate on how the invariance properties are aiding classification/representation? – whyyes Feb 25 at 16:02
    
Maybe I wasn't clear above, but it seems to me - if the interpretation is correct - the goal of invariant properties has been dropped. What seems to have been kept is the idea of variable transformations. – conjectures Feb 25 at 20:02
    
@conjectures, if you have a vector $\bar r$ in cartesian coordinates, then convert it to polar coordinates, the vector stays the same, i.e. it still point from the same point in the same direction. Are you saying that in machine learning the coordinate transformation changes the initial vector? – Aksakal Feb 25 at 20:18
    
but isn't that a property of the transformation more than the tensor? At least with linear and element-wise type transformations, which seem more popular in neural nets, they are equally present with vectors and matrices; what are the added benefits of the tensors? – whyyes Feb 26 at 9:47
    
@conjectures, PCA is just a rotation and projection. It's like rotating N-dimensional space to PC basis, then projecting to sub-space. Tensors are used in similar situations in physics, e.g. when looking at forces on the surfaces inside bodies etc. – Aksakal Feb 26 at 12:32

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