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: lowrank
DivideandConquer Learning by Anchoring a Conical Hull
Many wellknown machine learning methods aim to draw a line between two classes. However, in our recently accepted NIPS 2014 paper “DivideandConquer Learning by Anchoring a Conical Hull“, we reduce lots of fundamental machine learning problems (a broad class of … Continue reading
AISTATS 2013 GreBsmo code is released
Here is the GreBsmo code for our AISTATS 2013 paper. You can use it as a greedy version of GoDec solver for X=L+S problem. It is much faster and more robust. There are three video subsequences you can play in … Continue reading
[Best student paper award] Welcome to my “DivideandConquer Anchoring (DCA)” talk at ICDM Dallas Dec 8
Is it possible to finish a 60000×10000 matrix decomposition (NMF, PCA, etc) or completion in 6 seconds on your laptop’s matlab? Can we make it even faster by a simple distributable scheme? How to summarize a hugescale dataset (ratings, movie, … Continue reading
Greedy Bilateral (GreB) Paradigm for Largescale Matrix Completion, Robust PCA and Lowrank Approximation
Our paper “Greedy Bilateral Sketch, Completion and Smoothing” has been accepted by AISIATS 2013. Abstracts reads below, PDF is here, and code will be coming soon. Abstract: Recovering a large lowrank matrix from highly corrupted, incomplete or sparse outlier overwhelmed … Continue reading
SemiSoft GoDec: >4 times faster, autodetermined k
Here is a good news of GoDec (pertaining to our ICML 2011 paper): SemiSoft GoDec is released. Different from the ordinary GoDec which imposes hard threshholding to both the singular values of the lowrank part L and the entries of the … Continue reading
News about GoDec code and ICML 2011 paper
We recently published a google site for Go Decomposition (GoDec), presented on ICML 2011. Now you can find all the available information and upcoming news about GoDec on http://sites.google.com/site/godecomposition On the new site, there are 3 resources about GoDec we … Continue reading
GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case
This paper is accepted by ICML 2011 for presentation. Now the final version is ready and can be downloaded from here GO. Abstract: Lowrank and sparse structures have been profoundly studied in matrix completion and compressed sensing. In this paper, we develop … Continue reading
Multilabel Learning via Structured Decomposition and Group Sparsity
This paper is now available on arxiv： http://arxiv.org/abs/1103.0102 In multilabel learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly … Continue reading