Study notes for Sparse Coding

Sparse Coding

Sparse coding is a class of unsupervised methods for learning sets of over-complete bases to represent data efficiently. The aim of sparse coding is to find a set of basis vectors

such that an input vector

can be represented as a linear combination of these basis vectors:



The advantage of having an over-complete basis is that our basis vectors

are better able to capture structures and patterns inherent in the input data

.

However, with an over-complete basis, the coefficients are no longer uniquely determined by the input vector. Therefore, in sparse coding, we introduce the additional criterion of sparsity to resolve the degeneracy introduced by over-completeness.

The sparse coding cost function is defined on a set of m input vectors as:



where

is a sparsity function which penalizes

for being far from zero. We can interpret the first term of the sparse coding objective as a reconstruction term which tries to force the algorithm to provide a good representation of

, and the second term as a sparsity penalty which forces our representation of

(i.e., the learned features) to be sparse. The constant

is a scaling constant to determine the relative importance of these two contributions.

Although the most direct measure of sparsity is the

norm, it is non-differentiable and difficult to optimize in general. In practice, common choices for the sparsity cost

are the

penalty and the log sparsity


It is also possible to make the sparsity penalty arbitrary small by scaling down

and scaling up

by some large constant. To prevent this from happening, we will constrain

to be less than some constant

. The full sparse coding cost function hence is:





where the constant is usually set


One problem is that the constraint cannot be forced using simple gradient-based methods. Hence, in practice, this constraint is weakened to a "weight decay" term designed to keep the entries of

small:



Another problem is that the L1 norm is not differentiable at 0, and hence poses a problem for gradient-based methods. We will "smooth out" the L1 norm using an approximation which will allow us to use gradient descent. To "smooth out" the L1 norm, we use

in place of

, where

is a "smoothing parameter" which can also be interpreted as a sort of "sparsity parameter" (to see this, observe that when

is large compared to

, the

is dominated by

, and taking the square root yield approximately

.

Hence, the final objective function is:



The set of basis vectors are called "dictionary" (

).

is "adapted" to

if it can represent it with a few basis vectors, that is, there exists a sparse vector

in

such that

. We call

the sparse code. It is illustrated as follows:

Learning

Learning a set of basis vectors

using sparse coding consists of performing two separate optimizations (i.e., alternative optimization method):

The first being an optimization over coefficients

for each training example


The second being an optimization over basis vectors

across many training examples at once.

However, the classical optimization alternates between D and

can achieve good results, but very slow.

A significant limitation of sparse coding is that even after a set of basis vectors have been learnt, in order to "encode" a new data example, optimization must be performed to obtain the required coefficients. This significant "runtime" cost means that sparse coding is computationally expensive to implement even at test time, especially compared to typical feed-forward architectures.

Remarks

From my view, due to the sparseness enforced in the dictionary learning (i.e., sparse code), the restored matrix is able to remove noise of original matrix, i.e., having some effect of denoising. Hence, Sparse coding could be used to denoise images.

References

Sparse coding: http://ufldl.stanford.edu/wiki/index.php/Sparse_Coding

Sparse coding: autoencoder interpretation: http://ufldl.stanford.edu/wiki/index.php/Sparse_Coding:_Autoencoder_Interpretation

Sparse coding: exercise: http://ufldl.stanford.edu/wiki/index.php/Exercise:Sparse_Coding

Sparse coding and dictionary learning for image analysis, ICCV 2010 tutorial.