Leonardo R. Bachega

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Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs a novel non-linear sparsity constraint. More specifically, the covariance is constrained to have an eigen decomposition(More)
We describe the design of a dual-issue single-instruction, multiple-data-like (SIMD-like) extension of the IBM PowerPCt 440 floating-point unit (FPU) core and the compiler and algorithmic techniques to exploit it. This extended FPU is targeted at both the IBM massively parallel Blue Genet/L machine and the more pervasive embedded platforms. We discuss the(More)
A variety of problems in remote sensing require that a covariance matrix be accurately estimated, often from a limited number of data samples. We investigate the utility of several variants of a recently introduced covariance estimator-the sparse matrix transform (SMT), a shrinkage-enhanced SMT, and a graph-constrained SMT-in the context of several of these(More)
We describe the design, implementation, and evaluation of a dual-issue SIMD-like extension of the PowerPC 440 floating-point unit (FPU) core. This extended FPU is targeted at both IBM's massively parallel Blue-Gene/L machine as well as more pervasive embedded platforms. It has several novel features, such as a computational crossbar and cross-load/store(More)
Recently, the Sparse Matrix Transform (SMT) has been proposed as a tool for estimating the eigen-decomposition of high dimensional data vectors [1]. The SMT approach has two major advantages: First it can improve the accuracy of the eigendecomposition, particularlywhen the number of observations, n, is less the the vector dimension, p. Second, the resulting(More)
The BlueGene/L supercomputer will use system-on-a-chip integration and a highly scalable cellular architecture to deliver 360 Teraflops of peak computing power. With 65,536 compute nodes, BlueGene/L represents a new level of scalability for parallel systems. As such, it is natural for many scalability challenges to arise. In this paper, we discuss(More)
—This paper addresses two issues related to the detection of hyperspectral anomalies. The first issue is the evaluation of anomaly detector performance even when labeled data is not available. The second issue is the estimation of the covariance structure of the data in local detection methods, such as the RX detector, when the number of available training(More)
—We study a problem of detecting deterministic signals buried in correlated clutter using wireless sensor networks. We are specifically interested in developing a distributed algorithm over the network to detect the presence of a deterministic signal while keeping low communication delay and energy associated with the distributed computation. In this paper,(More)
This paper discusses the use of the Sparse Matrix Transform (SMT) to model the covariance structure of high-dimensional data in the likelihood ratio test used for hypothesis testing. The SMT has been shown to produce more accurate estimates of covariance matrices when the number of training samples n is much less than the number of dimensions p of the data.(More)