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234
Sparse PCA via Covariance Thresholding
, 2014
"... In sparse principal component analysis we are given noisy observations of a low-rank matrix of di-mension n×p and seek to reconstruct it under additional sparsity assumptions. In particular, we assume here that the principal components v1,...,vr have at most k1, · · · , kr non-zero entries respec ..."
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Cited by 1 (0 self)
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In sparse principal component analysis we are given noisy observations of a low-rank matrix of di-mension n×p and seek to reconstruct it under additional sparsity assumptions. In particular, we assume here that the principal components v1,...,vr have at most k1, · · · , kr non-zero entries
FASTER AGREEMENT VIA A SPECTRAL METHOD FOR DETECTING MALICIOUS BEHAVIOR
"... Abstract. We address the problem of Byzantine agreement, to bring processors to agreement on a bit in the presence of a strong adversary. This adversary has full information of the state of all processors, the ability to control message scheduling in an asynchronous model, and the ability to control ..."
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Cited by 2 (1 self)
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an exponential amount of computation per individual processor was required. In this paper, we improve that result to require both expected polynomial computation and communication time. We use a novel technique for detecting malicious behavior via spectral analysis. In particular, our algorithm uses coin flips
1Backing off from Infinity: Performance Bounds via Concentration of Spectral Measure for Random MIMO Channels
"... Abstract—The performance analysis of random vector chan-nels, particularly multiple-input-multiple-output (MIMO) chan-nels, has largely been established in the asymptotic regime of large channel dimensions, due to the analytical intractability of characterizing the exact distribution of the objectiv ..."
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outputs. Our results lead to simple, informative, and reasonably accurate control of various performance metrics in the finite-dimensional regime, as corroborated by the numerical simulations. Our analysis frame-work is established via the concentration of spectral measure phenomenon for random matrices
Robust Sparse PCA via Weighted Elastic Net
"... Abstract. In principal component analysis (PCA), `2/`1-norm is widely used to measure coding residual. In this case, it assume that the residual follows Gaussian/Laplacian distribution. However, it may fail to describe the coding errors in practice when there are outliers. Toward this end, this pape ..."
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Cited by 1 (0 self)
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Abstract. In principal component analysis (PCA), `2/`1-norm is widely used to measure coding residual. In this case, it assume that the residual follows Gaussian/Laplacian distribution. However, it may fail to describe the coding errors in practice when there are outliers. Toward this end
Foreground Detection by Robust PCA solved via a Linearized Alternating Direction Method
- INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND RECOGNITION, ICIAR 2012, PORTUGAL
, 2012
"... Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. RPCA ..."
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problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the l1-norm. To solve this convex program, an Alternating Direction Method (ADM) is commonly used. However, the subproblems in ADM are easily solvable only when the linear mappings in the constraints
Moving Object Detection by Robust PCA solved via a Linearized Symmetric Alternating Direction Method
, 2013
"... Robust Principal Components Analysis (RPCA) gives a suitable framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving objects constitute the correlated sparse outliers. RPCA problem ..."
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can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the l1-norm. This convex optimization is commonly solved by an Alternating Direction Method (ADM) that is not applicable in real application, because it is computationally expensive and needs a huge size
Non-Convex Rank Minimization via an Empirical Bayesian Approach
"... In many applications that require matrix solutions of minimal rank, the underlying cost function is non-convex leading to an intractable, NP-hard optimization problem. Consequently, the convex nuclear norm is frequently used as a surrogate penalty term for matrix rank. The problem is that in many pr ..."
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Cited by 5 (3 self)
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methodology is generally applicable to a wide range of low-rank applications, we focus our attention on the robust principal component analysis problem (RPCA), which involves estimating an unknown low-rank matrix with unknown sparse corruptions. Theoretical and empirical evidence are presented to show
port Vector Machines, Kernel Fisher Discriminant analysis
"... Abstract | This review provides an introduction to Sup- ..."
ESTIMATING HEART RATE VIA DEPTH VIDEO MOTION TRACKING
"... Depth sensors like Microsoft Kinect can acquire partial geo-metric information in a 3D scene via captured depth images, with potential application to non-contact health monitoring. However, captured depth videos typically suffer from low bit-depth representation and acquisition noise corruption, and ..."
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the depth video to deduce 3D motion vectors. The deduced 3D vectors are then analyzed via principal component analysis to estimate heart rate. Experimental results show improved tracking accuracy using our proposed joint bit-depth enhancement / denoising procedure, and estimated heart rates are close
Circuit level reliability analysis of Cu interconnects
- In Proceedings of the International Symposium on Quality Electronic Design
, 2004
"... Copper (Cu) based interconnect technology is expected to meet some of the challenges of technology scaling in the pursuit of higher performance. However, Cu interconnects are still susceptible to electromigration-induced failure over time. We describe a new hierarchical approach for predicting the r ..."
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Cited by 16 (2 self)
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the reliability of Cu-based interconnects in circuit layouts, and present an RCAD tool, SysRel, for such an analysis. We propose a (jL) product filtering algorithm with a classification of separate via-above and via-below treatments in Cu interconnect trees. After the filtering of immortal trees, a default model
Results 11 - 20
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234
