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
Tag Archives: clustering
Divide-and-Conquer Learning by Anchoring a Conical Hull
Many well-known machine learning methods aim to draw a line between two classes. However, in our recently accepted NIPS 2014 paper “Divide-and-Conquer Learning by Anchoring a Conical Hull“, we reduce lots of fundamental machine learning problems (a broad class of … 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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