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: compressed sensing
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
Compressed Labeling: An important extension of Hamming Compressed Sensing; at NIPS now
We are just informed that our submission “Compressed Labeling (CL) on Distilled Labelsets (DL) for Multilabel Learning” is accepted by Machine Learning Journal (Springer). Online first PDF can be downloaded here. CL is an important application and extension of Hamming … 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
Hamming Compressed Sensingrecovering kbit quantization from 1bit measurements with linear noniterative algorithm
We developed a new compressed sensing type signal acquisition paradigm called “Hamming Compressed Sensing (HCS)” to recover signal’s kbit quantization rather than itself. Directly recovering quantization is much more preferred in practical digital systems. HCS provides a linear, noniterative quantization … Continue reading
Posted in Tianyi's work
Tagged 1bit measurements, compressed sensing, fast algorithm, Quantization recovery
3 Comments
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
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