Tag Archives: compressed sensing

Greedy Bilateral (GreB) Paradigm for Large-scale Matrix Completion, Robust PCA and Low-rank 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 low-rank matrix from highly corrupted, incomplete or sparse outlier overwhelmed … Continue reading

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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 Multi-label 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

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Semi-Soft GoDec: >4 times faster, auto-determined k

Here is a good news of GoDec (pertaining to our ICML 2011 paper): Semi-Soft GoDec is released. Different from the ordinary GoDec which imposes hard threshholding to both the singular values of the low-rank part L and the entries of the … Continue reading

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Hamming Compressed Sensing-recovering k-bit quantization from 1-bit measurements with linear non-iterative algorithm

We developed a new compressed sensing type signal acquisition paradigm called “Hamming Compressed Sensing (HCS)” to recover signal’s k-bit quantization rather than itself. Directly recovering quantization is much more preferred in practical digital systems. HCS provides a linear, non-iterative quantization … Continue reading

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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

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GoDec: Randomized Low-rank & 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: Low-rank and sparse structures have been profoundly studied in matrix completion and compressed sensing. In this paper, we develop … Continue reading

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Multi-label Learning via Structured Decomposition and Group Sparsity

This paper is now available on arxiv: http://arxiv.org/abs/1103.0102 In multi-label 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

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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

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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

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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

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