Smooth PARAFAC Decomposition for Tensor Completion

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

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 کامل

A PARCUBE: Sparse Parallelizable CANDECOMP-PARAFAC Tensor Decomposition

How can we efficiently decompose a tensor into sparse factors, when the data does not fit in memory? Tensor decompositions have gained a steadily increasing popularity in data mining applications, however the current state-of-art decomposition algorithms operate on main memory and do not scale to truly large datasets. In this work, we propose PARCUBE, a new and highly parallelizable method for ...

متن کامل

Linked PARAFAC/CP Tensor Decomposition and Its Fast Implementation for Multi-block Tensor Analysis

In this paper we propose a new flexible group tensor analysis model called the linked CP tensor decomposition (LCPTD). The LCPTD method can decompose given multiple tensors into common factor matrices, individual factor matrices, and core tensors, simultaneously. We applied the Hierarchical Alternating Least Squares (HALS) algorithm to the LCPTD model; besides we impose additional constraints t...

متن کامل

Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion

Low-rank tensor estimation has been frequently applied in many real-world problems. Despite successful applications, existing Schatten 1-norm minimization (SNM) methods may become very slow or even not applicable for large-scale problems. To address this difficulty, we therefore propose an efficient and scalable core tensor Schatten 1-norm minimization method for simultaneous tensor decompositi...

متن کامل

A Parallel PARAFAC Implementation & Scalability Testing for Large-Scale Dense Tensor Decomposition

Parallel Factor Analysis (PARAFAC) is used in many scientific disciplines to decompose multidimensional datasets into principal factors in order to uncover relationships in the data. While quite popular, the common implementations of PARAFAC are single server solutions that do not scale well to very large datasets. To address this limitation, a Parallel PARAFAC algorithm has been designed and i...

متن کامل

ذخیره 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: 2016

ISSN: 1053-587X,1941-0476

DOI: 10.1109/tsp.2016.2586759