May 2017 S M T W T F S « Sep 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Pages
 1bit measurements Bregman iteration classification clustering compressed sensing computer vision conical hull problem convex optimization dimension reduction DivideandConquer elastic net fast algorithm fast SVD feature selection fixed point continuation game greedy search group sparsity Hamming Compressed Sensing iterative thresholding Kmeans latent variable model lowrank manifold learning matrix completion matrix factorization multilabel learning Ncut Nesterov's method NIPS 2011 Nonnegative Matrix Factorization optimization Quantization recovery randomized optimization robust principal component analysis SDP Separable assumption sparse learning Spectral clustering structured learning SVM
ClustrMaps

What’s new
 List of Submodular Optimization on Streaming Data (In Update)
 DivideandConquer Learning by Anchoring a Conical Hull
 Multitask Copula – A semiparametric joint prediction model for multiple outputs with sparse graph structure
 NeSVM (Nesterov’s method for SVM) code for our ICDM 2010 paper
 AISTATS 2013 GreBsmo code is released
Articles
Tag Archives: Nesterov’s method
NeSVM (Nesterov’s method for SVM) code for our ICDM 2010 paper
You can now download MATLAB code for NeSVM from here. In the code, options.mu is a key parameter to adjust the tradeoff between consistent decreasing of primal object function, and the speed. So you need to roughly tune it to … Continue reading
NESVM: a Fast Gradient Method for Support Vector Machines
This is a work we finished one year ago and is accepted as a regular paper in ICDM 2010. NESVM is a fast SVM algorithm for primal linear and kernel SVM problems, with the optimal convergence rate of firstorder method … Continue reading
Posted in Tianyi's work
Tagged classification, computer vision, fast algorithm, Nesterov's method, SVM
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Compressed sensing review (1): Reconstruction Algorithms
CS reconstruction algorithms are always the most astonishing thing for people who know compressed sensing at the first time. Because only a few sampling (much less than ShannonNyquist sampling rate) can perfectly reconstruct the whole signal is really a big … Continue reading
Fast Gradient Clustering
This is a work we have posted and given a spotlight talk on NIPS 09 Workshop on Discrete Optimization in Machine Learning: Structures, Algorithms and Applications (DISCML). You can find it on: http://www.cs.caltech.edu/~krausea/discml/papers/zhou09fast.pdf Fast Gradient Clustering (FGC) tackles the two … Continue reading
Posted in Tianyi's work
Tagged clustering, fast algorithm, Kmeans, Ncut, Nesterov's method, optimization, SDP, Spectral clustering
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