May 2024 S M T W T F S 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
- 1-bit measurements
- Bregman iteration
- classification
- clustering
- compressed sensing
- computer vision
- conical hull problem
- convex optimization
- dimension reduction
- Divide-and-Conquer
- elastic net
- fast algorithm
- fast SVD
- feature selection
- fixed point continuation
- game
- greedy search
- group sparsity
- Hamming Compressed Sensing
- iterative thresholding
- K-means
- latent variable model
- low-rank
- manifold learning
- matrix completion
- matrix factorization
- multi-label learning
- N-cut
- 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
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What’s new
- List of Submodular Optimization on Streaming Data (In Update)
- Divide-and-Conquer Learning by Anchoring a Conical Hull
- Multi-task 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
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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 trade-off 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 first-order 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 Shannon-Nyquist 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, K-means, N-cut, Nesterov's method, optimization, SDP, Spectral clustering
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Tianyi Zhou's Research Blog 