This class implements the Nonsmooth Nonnegative Matrix Factorization (nsNMF) model, required by the Nonsmooth NMF algorithm.
The Nonsmooth NMF algorithm is defined by Pascual-Montano et al. (2006) as a modification of the standard divergence based NMF algorithm (see section Details and references below). It aims at obtaining sparser factor matrices, by the introduction of a smoothing matrix.
The Nonsmooth NMF algorithm is a modification of the
standard divergence based NMF algorithm (see
NMF-class). Given a non-negative n x p matrix V and a factorization rank
r, it fits the following model:
V ~ W S(theta) H, where:
W and H are such as in the standard
model, i.e. non-negative matrices of dimension n x r and r x p
respectively;
S is a r \times r square matrix whose
entries depends on an extra parameter 0\leq \theta
\leq 1 in the following way: S = (1-\theta)I +
\frac{\theta}{r} 11^T , where I is the identity
matrix and 1 is a vector of ones.
The interpretation of S as a smoothing matrix can be
explained as follows: Let X be a positive, nonzero,
vector. Consider the transformed vector Y = S X. If
\theta = 0, then Y = X and no smoothing on
X has occurred. However, as theta tends to 1, the vector Y tends to the
constant vector with all elements almost equal to the
average of the elements of X. This is the smoothest
possible vector in the sense of non-sparseness because
all entries are equal to the same nonzero value, instead
of having some values close to zero and others clearly
nonzero.
signature(object = "NMFns"): Compute
estimate for an NMFns object, according to the Nonsmooth
NMF model (cf. NMFns-class).
Extra arguments in ... are passed to method
smoothing, and are typically used to pass a value
for theta, which is used to compute the smoothing
matrix instead of the one stored in object.
signature(object = "NMFns"): Show
method for objects of class NMFns
Object of class NMFns can be created using the
standard way with operator new
However, as for all NMF model classes -- that extend
class NMF-class, objects of class
NMFns should be created using factory method
nmfModel :
new('NMFns')
nmfModel(model='NMFns')
nmfModel(model='NMFns', W=w, theta=0.3
See nmfModel for more details on how to use
the factory method.
The Nonsmooth NMF algorithm uses a modified version of
the multiplicative update equations in Lee & Seung's
method for Kullback-Leibler divergence minimization. The
update equations are modified to take into account the --
constant -- smoothing matrix. The modification reduces to
using matrix W S instead of matrix W in the
update of matrix H, and similarly using matrix
S H instead of matrix H in the update of
matrix W.
After the matrix W has been updated, each of its
columns is scaled so that it sums up to 1.
Pascual-Montano A, Carazo JM, Kochi K, Lehmann D and Pascual-marqui RD (2006). "Nonsmooth nonnegative matrix factorization (nsNMF)." _IEEE Trans. Pattern Anal. Mach. Intell_, *28*, pp. 403-415.
# create a completely empty NMFns object
new('NMFns')
## <Object of class:NMFns>
## features: 0
## basis/rank: 0
## samples: 0
## theta: 0.5
# create a NMF object based on random (compatible) matrices
n <- 50; r <- 3; p <- 20
w <- rmatrix(n, r)
h <- rmatrix(r, p)
nmfModel(model='NMFns', W=w, H=h)
## <Object of class:NMFns>
## features: 50
## basis/rank: 3
## samples: 20
## theta: 0.5
# apply Nonsmooth NMF algorithm to a random target matrix
V <- rmatrix(n, p)
## Not run: nmf(V, r, 'ns')
# random nonsmooth NMF model
rnmf(3, 10, 5, model='NMFns', theta=0.3)
## <Object of class:NMFns>
## features: 10
## basis/rank: 3
## samples: 5
## theta: 0.3