Cramér-Rao-Induced Bounds for CANDECOMP/PARAFAC Tensor Decomposition
نویسندگان
چکیده
منابع مشابه
Cramér-Rao-Induced Bounds for CANDECOMP/PARAFAC Tensor Decomposition
This paper presents a Cramér-Rao lower bound (CRLB) on the variance of unbiased estimates of factor matrices in Canonical Polyadic (CP) or CANDECOMP/PARAFAC (CP) decompositions of a tensor from noisy observations, (i.e., the tensor plus a random Gaussian i.i.d. tensor). A novel expression is derived for a bound on the mean square angular error of factors along a selected dimension of a tensor o...
متن کاملCramér-Rao bounds for radar altimeter waveforms
The pseudo maximum likelihood estimator allows one to estimate the unknown parameters of Brown’s model for altimeter waveforms. However, the optimality of this estimator, for instance in terms of minimizing the mean square errors of the unknown parameters is not guarantied. Thus it is not clear whether there is some space for developing new estimators for the unknown parameters of altimetric si...
متن کاملCramér-Rao bounds for synchronization of rotations
Synchronization of rotations is the problem of estimating a set of rotations Ri ∈ SO(n), i = 1 . . . N based on noisy measurements of relative rotations RiR > j . This fundamental problem has found many recent applications, most importantly in structural biology. We provide a framework to study synchronization as estimation on Riemannian manifolds for arbitrary n under a large family of noise m...
متن کاملCramér-Rao-Type Bounds for Localization
1 The localization problem is fundamentally important for sensor networks. This paper studies the Cramér-Rao lower bound (CRB) for two kinds of localization based on noisy range measurements. The first is Anchored Localization in which the estimated positions of at least 3 nodes are known in global coordinates. We show some basic invariances of the CRB in this case and derive lower and upper bo...
متن کاملNetwork Localization Cramér-Rao Bounds for General Measurement Models
A closed-form Cramér-Rao bound (CRB) for general multi-modal measurements is derived for D-dimensional network localization (D ∈ {2,3}). Links are asymmetric and the measurements among neighboring nodes are non-reciprocal depending on an arbitrary differentiable function of their position difference, subject to additive Gaussian noise (with variance that may depend on distance). The provided bo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2013
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2013.2245660