搜索结果: 1-10 共查到“统计学 Compressive”相关记录10条 . 查询时间(0.072 秒)
Compressive Network Analysis
network data analysis compressive sensing Radon basis pursuit restricted isometry property clique detection
2016/1/25
Modern data acquisition routinely produces massive amounts of network data.Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected ...
Compressive Network Analysis
network data analysis compressive sensing Radon basis pursuit
2016/1/20
Modern data acquisition routinely produces massive amounts of network data.Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected ...
This paper addresses the problem of unsupervised feature learning for text data.Our method is grounded in the principle of minimum description length and uses a dictionary-based compression scheme to ...
Recovering Graph-Structured Activations using Adaptive Compressive Measurements
Recovering Graph-Structured Activations Adaptive Compressive Measurements
2013/6/13
We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activate...
Compressive Shift Retrieval
Compressed sensing shift retrieval sig-nal reconstruction signal registration
2013/5/2
The classical shift retrieval problem considers two signals in vector form that are related by a cyclic shift. In this paper, we develop a compressive variant where the measurement of the signals is u...
Signal Recovery in Unions of Subspaces with Applications to Compressive Imaging
Union of Subspaces Group Sparsity Convex Optimization Structured Sparsity Compressed Sensing
2012/11/22
In applications ranging from communications to genetics, signals can be modeled as lying in a union of subspaces. Under this model, signal coefficients that lie in certain subspaces are active or inac...
Robust Dequantized Compressive Sensing
compressive sensing signal reconstruction quantization optimization.
2012/9/18
We consider the reconstruction problem in compressed sensing in which the observations are recorded in a finite number of bits. They may thus contain quantization errors (from being rounded to the nea...
Reconstruction of Fractional Brownian Motion Signals From Its Sparse Samples Based on Compressive Sampling
Compressive Sampling fractional Brownian motion interpolation financial time-series fractal
2011/6/21
This paper proposes a new fBm (fractional Brownian
motion) interpolation/reconstruction method from partially
known samples based on CS (Compressive Sampling). Since 1/f
property implies power law ...
Manifold-Based Signal Recovery and Parameter Estimation from Compressive Measurements
Manifolds dimensionality reduction random projections Compressive Sensing spar-sity signal recovery parameter estimation
2010/3/10
A field known as Compressive Sensing (CS) has recently emerged to help address the growing
challenges of capturing and processing high-dimensional signals and data sets. CS exploits the
surprising f...
Compressive Sensing Using Low Density Frames
Low density frames compressive sensing sumproduct algorithm expectation maximization Gaussian scalemixtures
2010/3/19
We consider the compressive sensing of a sparse or
compressible signal x 2 RM. We explicitly construct a class of
measurement matrices, referred to as the low density frames,
and develop decoding a...