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Make Some ROOM for the Zeros: Data Sparsity in Secure Distributed Machine Learning
secure computation machine learning
2019/3/13
Exploiting data sparsity is crucial for the scalability of many data analysis tasks. However, while there is an increasing interest in efficient secure computation protocols for distributed machine le...
ENDMEMBER EXTRACTION OF HIGHLY MIXED DATA USING L1 SPARSITY-CONSTRAINED MULTILAYER NONNEGATIVE MATRIX FACTORIZATION
Hyperspectral Imagery Nonnegative Matrix Factorization Multilayer Nonnegative Matrix Factorization Sparsity Constraint Endmember Extraction
2018/5/11
Due to the limited spatial resolution of remote hyperspectral sensors, pixels are usually highly mixed in the hyperspectral images. Endmember extraction refers to the process identifying the pure endm...
Tests atternative to higher criticism for high dimensional means under sparsity and column-wise dependence
Large deviation Large p, small n Optimal detection boundary Sparse signal Thresholding Weak dependence
2016/1/25
We consider two alternative tests to the Higher Criticism test of Donoho and Jin (2004) for high dimensional means under the spar-sity of the non-zero means for sub-Gaussian distributed data with unkn...
Tests atternative to higher criticism for high dimensional means under sparsity and column-wise dependence
Large deviation Large p, small n Optimal detection boundary Sparse signal Thresholding Weak dependence
2016/1/20
We consider two alternative tests to the Higher Criticism test of Donoho and Jin (2004) for high dimensional means under the spar-sity of the non-zero means for sub-Gaussian distributed data with unkn...
Adapting to Unknown Sparsity by controlling the False Discovery Rate
Thresholding Wavelet Denoising Minimax Estimation
2015/8/21
We attempt to recover a high-dimensional vector observed in white noise, where
the vector is known to be sparse, but the degree of sparsity is unknown. We consider
three di®erent ways of deˉnin...
Enhancing sparsity by reweighted l1 minimization
1-Minimization ·Iterative reweighting Underdetermined systems of linear equations·Compressive sensing Dantzig selector· Sparsity FOCUSS
2015/8/10
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constraine...
Wave atoms and sparsity of oscillatory patterns
Wave atoms Image processing Texture Oscillatory Warping Diffeomorphism
2015/7/14
We introduce “wave atoms” as a variant of 2D wavelet packets obeying the parabolic scaling wavelength ～ (diameter)2. We prove that warped oscillatory functions, a toy model for texture, have a signifi...
Enhancing Sparsity by Reweighted l1 Minimization
Iterative reweighting Underdetermined systems of linear equations Compressive sensing Dantzig selector Sparsity
2015/7/9
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constraine...
The lasso penalizes a least squares regression by the sum of the absolute values
(L1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many
coefficien...
On Computation of Optimal Controllers Subject to Quadratically Invariant Sparsity Constraints
Quadratically Invariant Sparsity Optimal Controllers
2015/6/19
We consider the problem of constructing optimal sparse controllers. It is known that a property called quadratic invariance of the constraint set is important, and results in the constrained minimum-n...
Sparsity and Incoherence in Compressive Sampling
`1-minimization basis pursuit restricted orthonormality sparsity singular values of random matrices wavelets discrete Fourier transform
2015/6/17
We consider the problem of reconstructing a sparse signal x0 ∈ Rn from a limited number of linear measurements. Given m randomly selected samples of Ux0, where U is an orthonormal matrix, we show that...
Enhancing Sparsity by Reweighted ℓ1 Minimization
ℓ 1-minimization iterative reweighting underdetermined systems of linear equations Compressive Sensing the Dantzig selector sparsity FOCUSS
2015/6/17
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constraine...
Mathematics of sparsity (and a few other things)
Underdetermined systems of linear equations compressive sensing matrix completion sparsity low-rank-matrices 1 norm nuclear norm convex programing Gaussian widths
2015/6/17
In the last decade, there has been considerable interest in understanding when it is possible to find structured solutions to underdetermined systems of linear equations. This paper surveys some of th...
Problems in Generic Combinatorial Rigidity: Sparsity, Sliders, and Emergence of Components
Algorithms Geometry Graph Theory Matroids Random Graphs Rigidity Theory
2014/12/18
Rigidity theory deals in problems of the following form: given a structure defined by geometric constraints on a set of objects, what information about its geometric behavior is implied by the underly...
Expectation Propagation for Neural Networks with Sparsity-promoting Priors
expectation propagation neural network multilayer perceptron linear model sparse prior automatic relevance determination
2013/4/28
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation ...