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» A Practical Method for the Sparse Resultant
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CORR
2011
Springer
203views Education» more  CORR 2011»
14 years 4 months ago
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse s...
Laurent Jacques, Jason N. Laska, Petros Boufounos,...
JC
2010
55views more  JC 2010»
14 years 8 months ago
Dimension-wise integration of high-dimensional functions with applications to finance
We present a new general class of methods for the computation of high-dimensional integrals. The quadrature schemes result by truncation and discretization of the anchored-ANOVA d...
Michael Griebel, Markus Holtz
DAC
2004
ACM
15 years 10 months ago
Sparse transformations and preconditioners for hierarchical 3-D capacitance extraction with multiple dielectrics
Capacitance extraction is an important problem that has been extensively studied. This paper presents a significant improvement for the fast multipole accelerated boundary element...
Shu Yan, Vivek Sarin, Weiping Shi
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
15 years 10 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen
ICASSP
2010
IEEE
14 years 10 months ago
Robust regression using sparse learning for high dimensional parameter estimation problems
Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for low-dimensional robust regression problems. However, the comb...
Kaushik Mitra, Ashok Veeraraghavan, Rama Chellappa