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2,311
Algorithms for Nonnegative Matrix Factorization
 In NIPS
, 2001
"... Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minim ..."
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Cited by 1246 (5 self)
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Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown
A Data Locality Optimizing Algorithm
, 1991
"... This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation algorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifi ..."
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Cited by 804 (16 self)
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, and Givens QR factorization. Performance evaluation indicates that locality optimization is especially crucial for scaling up the performance of parallel code.
FAST VOLUME RENDERING USING A SHEARWARP FACTORIZATION OF THE VIEWING TRANSFORMATION
, 1995
"... Volume rendering is a technique for visualizing 3D arrays of sampled data. It has applications in areas such as medical imaging and scientific visualization, but its use has been limited by its high computational expense. Early implementations of volume rendering used bruteforce techniques that req ..."
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Cited by 542 (2 self)
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casting algorithms because the latter must perform analytic geometry calculations (e.g. intersecting rays with axisaligned boxes). The new scanlineorder algorithm simply streams through the volume and the image in storage order. We describe variants of the algorithm for both parallel and perspective
Scalable molecular dynamics with NAMD.
 J Comput Chem
, 2005
"... Abstract: NAMD is a parallel molecular dynamics code designed for highperformance simulation of large biomolecular systems. NAMD scales to hundreds of processors on highend parallel platforms, as well as tens of processors on lowcost commodity clusters, and also runs on individual desktop and la ..."
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Cited by 849 (63 self)
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Abstract: NAMD is a parallel molecular dynamics code designed for highperformance simulation of large biomolecular systems. NAMD scales to hundreds of processors on highend parallel platforms, as well as tens of processors on lowcost commodity clusters, and also runs on individual desktop
Bullet: High Bandwidth Data Dissemination Using an Overlay Mesh
, 2003
"... In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes selforganize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burd ..."
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Cited by 424 (22 self)
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deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to selforganize into a high bandwidth overlay mesh. We construct Bullet around the insight that data
Online learning for matrix factorization and sparse coding
, 2010
"... Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the largescale matrix factorization problem that consists of learning the basis set in order to ad ..."
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Cited by 330 (31 self)
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to adapt it to specific data. Variations of this problem include dictionary learning in signal processing, nonnegative matrix factorization and sparse principal component analysis. In this paper, we propose to address these tasks with a new online optimization algorithm, based on stochastic approximations
Improving MapReduce Performance in Heterogeneous Environments
, 2008
"... MapReduce is emerging as an important programming model for largescale dataparallel applications such as web indexing, data mining, and scientific simulation. Hadoop is an opensource implementation of MapReduce enjoying wide adoption and is often used for short jobs where low response time is cri ..."
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Cited by 350 (19 self)
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assumptions do not always hold. An especially compelling setting where this occurs is a virtualized data center, such as Amazon’s Elastic Compute Cloud (EC2). We show that Hadoop’s scheduler can cause severe performance degradation in heterogeneous environments. We design a new scheduling algorithm, Longest
On the equivalence of nonnegative matrix factorization and spectral clustering
 in SIAM International Conference on Data Mining
, 2005
"... Current nonnegative matrix factorization (NMF) deals with X = FG T type. We provide a systematic analysis and extensions of NMF to the symmetric W = HH T, and the weighted W = HSHT. We show that (1) W = HHT is equivalent to Kernel Kmeans clustering and the Laplacianbased spectral clustering. (2) X ..."
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Cited by 159 (20 self)
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Current nonnegative matrix factorization (NMF) deals with X = FG T type. We provide a systematic analysis and extensions of NMF to the symmetric W = HH T, and the weighted W = HSHT. We show that (1) W = HHT is equivalent to Kernel Kmeans clustering and the Laplacianbased spectral clustering. (2
Convex and SemiNonnegative Matrix Factorizations
, 2008
"... We present several new variations on the theme of nonnegative matrix factorization (NMF). Considering factorizations of the form X = F GT, we focus on algorithms in which G is restricted to contain nonnegative entries, but allow the data matrix X to have mixed signs, thus extending the applicable ra ..."
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Cited by 112 (10 self)
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We present several new variations on the theme of nonnegative matrix factorization (NMF). Considering factorizations of the form X = F GT, we focus on algorithms in which G is restricted to contain nonnegative entries, but allow the data matrix X to have mixed signs, thus extending the applicable
Nonnegative sparse coding
 PROC. IEEE WORKSHOP ON NEURAL NETWORKS FOR SIGNAL PROCESSING (NNSP’2002), 2002
, 2002
"... Nonnegative sparse coding is a method for decomposing multivariate data into nonnegative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and nonnegative matrix factorization. We then give a sim ..."
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Cited by 166 (3 self)
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Nonnegative sparse coding is a method for decomposing multivariate data into nonnegative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and nonnegative matrix factorization. We then give a
Results 1  10
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2,311