November 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 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: optimization
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
Compressed sensing review (0)
I plan to write an easy understanding review of compressed sensing, which is a popular topic that attracts considerable attention from 2004 and appears being related to more and more fields in recent years. The review will be posted in … Continue reading
Compressed sensing games
Compressed sensing has been proved having intimate connections to tremendous problems in signal processing and harmonic analysis. The fundamental idea of compressed sensing and its extension matrix completion can be illustrated as: It is possible to recover a signal (e.g., … 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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