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NIPS
2001
15 years 5 months ago
Spectral Relaxation for K-means Clustering
The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is then used to nd local minima. In this paper we sh...
Hongyuan Zha, Xiaofeng He, Chris H. Q. Ding, Ming ...
JMLR
2006
108views more  JMLR 2006»
15 years 4 months ago
Learning Spectral Clustering, With Application To Speech Separation
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same clu...
Francis R. Bach, Michael I. Jordan
IR
2010
15 years 2 months ago
Gradient descent optimization of smoothed information retrieval metrics
Abstract Most ranking algorithms are based on the optimization of some loss functions, such as the pairwise loss. However, these loss functions are often different from the criter...
Olivier Chapelle, Mingrui Wu
POPL
2009
ACM
16 years 4 months ago
Modular code generation from synchronous block diagrams: modularity vs. code size
We study modular, automatic code generation from hierarchical block diagrams with synchronous semantics. Such diagrams are the fundamental model behind widespread tools in the emb...
Roberto Lublinerman, Christian Szegedy, Stavros Tr...
APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
15 years 9 months ago
Sampling Bounds for Stochastic Optimization
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
Moses Charikar, Chandra Chekuri, Martin Pál