Probabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors
- B.Tech / M.Tech / M.Sc / Ph.D
- GANGADHAR.T
- 12 Months ago
Tensor canonical polyadic decomposition (CPD),
which recovers the latent factor matrices from multidimensional
data, is an important tool in signal processing. In many applications,
some of the factor matrices are known to have orthogonality
structure, and this information can be exploited to improve the
accuracy of latent factors recovery. However, existing methods for
CPD with orthogonal factors all require the knowledge of tensor
rank, which is difficult to acquire, and have no mechanism to handle
outliers in measurements. To overcome these disadvantages, in
this paper, a novel tensor CPD algorithm based on the probabilistic
inference framework is devised.
Know More