September 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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