ALORA: Affine Low-Rank Approximations
نویسندگان
چکیده
منابع مشابه
Generalized Low-Rank Approximations
We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving weighted low rank approximation problems, which, unlike simple matrix factorization problems, do not admit a closed form solution in general. We analyze, in addition, the nature of locally optimal solutions that arise in this context, demonstra...
متن کاملWeighted Low-Rank Approximations
We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving weighted low-rank approximation problems, which, unlike their unweighted version, do not admit a closedform solution in general. We analyze, in addition, the nature of locally optimal solutions that arise in this context, demonstrate the utility ...
متن کاملLow-Rank Approximations for Spatial Channel Models
We analyze the problem of approximating vectors that are superpositions of many closely spaced steering vectors. Such vectors appear in realistic models for wireless communication channels and describe single clusters of scatterers. We question the practice of using a dictionary of steering vectors in order to find a sparse approximation. Alternative dictionaries can be obtained from the Karhun...
متن کاملAutomated Disease Normalization with Low Rank Approximations
While machine learning methods for named entity recognition (mention-level detection) have become common, machine learning methods have rarely been applied to normalization (concept-level identification). Recent research introduced a machine learning method for normalization based on pairwise learning to rank. This method, DNorm, uses a linear model to score the similarity between mentions and ...
متن کاملRobust Generalized Low Rank Approximations of Matrices
In recent years, the intrinsic low rank structure of some datasets has been extensively exploited to reduce dimensionality, remove noise and complete the missing entries. As a well-known technique for dimensionality reduction and data compression, Generalized Low Rank Approximations of Matrices (GLRAM) claims its superiority on computation time and compression ratio over the SVD. However, GLRAM...
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: Journal of Scientific Computing
سال: 2018
ISSN: 0885-7474,1573-7691
DOI: 10.1007/s10915-018-0885-5