搜索结果: 1-15 共查到“数学 sparse”相关记录83条 . 查询时间(0.078 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Sparse Tensor Optimization Approaches for Traffic Flow Prediction, Anomaly Detection and Video Surveillance
交通流预测 异常检测 视频监控 稀疏张量 优化方法
2023/4/13
Tensor-based modeling and computation emerge prominently with urgent demands from practical applications in the big data era. With the intrinsic sparsity in real data sets and the dimensionality reduc...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：A Sparse Expansion of (Deep) Gaussian Processes
深层 高斯过程 稀疏展开
2023/4/18
Conventional inferential methods for (deep) Gaussian Processes models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inferen...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Maximum cuts in 3-uniform hypergraphs with sparse neighbourhoods
稀疏邻域 均匀超图 最大切口
2023/4/27
A cut of a hypergraph is a partition of its vertex set into two parts, and the size of the cut is the number of edges which have nonempty intersection with each of the two parts. A classical result of...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Distributed Sparse Identification for Stochastic Dynamic Systems under Cooperative Non-Persistent Excitation Condition
协作 非持续激励条件 随机动态系统 分布式 稀疏辨识
2023/4/28
This talk considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic s...
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Sparse graphical model Reversible Markov chain Markov equivalence class
2016/1/20
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
GENERALIZED SPARSE SIGNAL MIXING MODEL AND APPLICATION TO NOISY BLIND SOURCE SEPARATION
GENERALIZED SPARSE SIGNAL MIXING MODEL NOISY BLIND SOURCE SEPARATION
2015/9/29
Sparse constraints on signal decompositions are justified bytypical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to ...
Convolutive Demixing with Sparse Discrete Prior Models for Markov Sources
Convolutive Demixing Sparse Discrete Prior Models Markov Sources
2015/9/29
In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable with...
SOURCE SEPARATION USING SPARSE DISCRETE PRIOR MODELS
SOURCE SEPARATION SPARSE DISCRETE PRIOR MODELS
2015/9/29
In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variablewith ...
Estimator for Number of Sources using Minimum Description Length Criterion for Blind Sparse Source Mixtures
Minimum Description Length Criterion Blind Sparse Source Mixtures
2015/9/29
In this paper I present a Minimum Description Length Estimator for number of sources in an anechoic mixture of sparse signals.The criterion is roughly equal to the sum of negative normalized maximum l...
Information Theory Based Estimator of the Number of Sources in a Sparse Linear Mixing Model
Information Theory Estimator of the Number of Sources Sparse Linear Mixing Model
2015/9/29
In this paper we present an Information Theoretic Estimator for the number of sources mutuallydisjoint in a linear mixing model. The approach follows the Minimum Description Length prescription and is...
OPTIMAL BEAM PATTERN DESIGN FOR VERY LARGE SENSOR ARRAYS WITH SPARSE SAMPLING
OPTIMAL BEAM PATTERN DESIGN VERY LARGE SENSOR ARRAYS SPARSE SAMPLING
2015/9/29
Consider a large scale sensor array having N sensors that monitors a surveillance area. Using all sensors simultaneously may be unreasonable in terms of power consumption and data processing.For examp...
Equivalence of Reconstruction from the Absolute Value of the Frame Coefficients to a Sparse Representation Problem
frames nonlinear processing sparse representation
2015/9/29
The purpose of this note is to prove, for real frames, that signal reconstruction from the absolute value ofthe frame coefficients is equivalent to solution of a sparse signal optimization problem, na...
Higher Criticism for Detecting Sparse Heterogeneous Mixtures
Multiple Comparsions Combining Many p-values
2015/8/21
Higher Criticism, or second-level significance testing, is a multiple comparisons concept
mentioned in passing by Tukey (1976). It concerns a situation where there are many independent tests of...
Optimally Sparse Representation in General (non-Orthogonal) Dictionaries via
Sparse Representation Overcomplete Representation
2015/8/21
Given a ‘dictionary’ D = {dk} of vectors dk, we seek to represent a signal S as a
linear combination S =
k γ(k)dk, with scalar coefficients γ(k). In particular, we aim for
the sparsest repre...
Fast Solution of `1-norm Minimization Problems When the Solution May be Sparse
LASSO. LARS Homotopy Methods Basis Pursuit.
2015/8/21
The minimum `1-norm solution to an underdetermined system of linear equations y = Ax,
is often, remarkably, also the sparsest solution to that system. This sparsity-seeking property
is of interest i...