From yesterday's Nuit Blanche, I found I missed another sparse session in ICASSP 2012 in my previous post:

This blog reports latest progresses in

(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home

(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures

(3) My Works

## Tuesday, January 31, 2012

## Thursday, January 26, 2012

### Andrew Ng: Machine learning and AI via large scale brain simulations

The location is changed to:

Time: Monday, January 30th, 2012, 11:00 am

Abstract

By building large-scale simulations of cortical (brain) computations, can

we enable revolutionary progress in AI and machine learning? Machine

learning often works very well, but can be a lot of work to apply because

it requires spending a long time engineering the input representation (or

"features") for each specific problem. This is true for machine learning

applications in vision, audio, text/NLP and other problems.

To address this, researchers have recently developed "unsupervised feature

learning" and "deep learning" algorithms that can automatically learn

feature representations from unlabeled data, thus bypassing much of this

time-consuming engineering. Many of these algorithms are developed using

simple simulations of cortical (brain) computations, and build on such

ideas as sparse coding and deep belief networks. By doing so, they exploit

large amounts of unlabeled data (which is cheap and easy to obtain) to

learn a good feature representation. These methods have also surpassed the

previous state-of-the-art on a number of problems in vision, audio, and

text. In this talk, I describe some of the key ideas behind unsupervised

feature learning and deep learning, and present a few algorithms. I also

speculate on how large-scale brain simulations may enable us to make

significant progress in machine learning and AI, especially perception.

This talk will be broadly accessible, and will not assume a machine

learning background.

**CALIT2 ~ Atkinson Hall Auditorium**Time: Monday, January 30th, 2012, 11:00 am

Abstract

By building large-scale simulations of cortical (brain) computations, can

we enable revolutionary progress in AI and machine learning? Machine

learning often works very well, but can be a lot of work to apply because

it requires spending a long time engineering the input representation (or

"features") for each specific problem. This is true for machine learning

applications in vision, audio, text/NLP and other problems.

To address this, researchers have recently developed "unsupervised feature

learning" and "deep learning" algorithms that can automatically learn

feature representations from unlabeled data, thus bypassing much of this

time-consuming engineering. Many of these algorithms are developed using

simple simulations of cortical (brain) computations, and build on such

ideas as sparse coding and deep belief networks. By doing so, they exploit

large amounts of unlabeled data (which is cheap and easy to obtain) to

learn a good feature representation. These methods have also surpassed the

previous state-of-the-art on a number of problems in vision, audio, and

text. In this talk, I describe some of the key ideas behind unsupervised

feature learning and deep learning, and present a few algorithms. I also

speculate on how large-scale brain simulations may enable us to make

significant progress in machine learning and AI, especially perception.

This talk will be broadly accessible, and will not assume a machine

learning background.

## Tuesday, January 24, 2012

### Literature-CS: Sparse Signal Recovery/Compressed Sensing of ICASSP 2012

ICASSP 2012 has posted the technical program: http://www.icassp2012.com/RegularProgram.asp

Here are the sections on sparse signal recovery/compressed sensing:

SPTM-P6: Joint SPTM/SPCOM Session: Sampling Sparsity and Reconstruction II

SPCOM-P2: Sampling, Coding and Modulation

SPTM-L3: Compressed Sensing and Sparsity I

SPTM-L4: Compressed Sensing and Sparsity II

SPTM-L5: Compressed Sensing and Sparsity III

SPCOM-L4: Sparse Signal Processing for Communications and Networking

SPTM-P9: Sampling and Reconstruction

SAM-P5: Joint SAM/SPTM Session: Compressed Sensing and Sparse Signal Modeling

My paper will be presented at the section: SPTM-L4: Compressed Sensing and Sparsity II

The title is:

You can read it now from my website: http://sccn.ucsd.edu/%7Ezhang/Zhang_ICASSP2012.pdf

Codes can be downloaded at: http://sccn.ucsd.edu/%7Ezhang/BSBL_EM_Code.zip

The paper is the early work of the journal version:

The paper can be obtain from: http://arxiv.org/abs/1201.0862

Here are the sections on sparse signal recovery/compressed sensing:

SPTM-P6: Joint SPTM/SPCOM Session: Sampling Sparsity and Reconstruction II

SPCOM-P2: Sampling, Coding and Modulation

SPTM-L3: Compressed Sensing and Sparsity I

SPTM-L4: Compressed Sensing and Sparsity II

SPTM-L5: Compressed Sensing and Sparsity III

SPCOM-L4: Sparse Signal Processing for Communications and Networking

SPTM-P9: Sampling and Reconstruction

SAM-P5: Joint SAM/SPTM Session: Compressed Sensing and Sparse Signal Modeling

My paper will be presented at the section: SPTM-L4: Compressed Sensing and Sparsity II

The title is:

**Z.Zhang, B.D.Rao, Recovery of Block Sparse Signals Using the Framework of Block Sparse Bayesian Learning**You can read it now from my website: http://sccn.ucsd.edu/%7Ezhang/Zhang_ICASSP2012.pdf

Codes can be downloaded at: http://sccn.ucsd.edu/%7Ezhang/BSBL_EM_Code.zip

The paper is the early work of the journal version:

**Z.Zhang, B.D.Rao, Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation**The paper can be obtain from: http://arxiv.org/abs/1201.0862

## Tuesday, January 10, 2012

### A New Paper: Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation

We just finished a paper on block sparse model, which considers to exploit

The associated codes can be downloaded here:

Here is the abstract:

The paper can be downloaded here:

In this paper, we proposed three algorithms (BSBL-EM, BSBL-BO, BSBL-L1) for the block sparse model when block partition is known, and three algorithms (EBSBL-EM, EBSBL-BO, EBSBL-L1) for the model when block partition is unknown.

Here are some highlights:

I have spent more than one month to read published algorithms, downloaded their codes, performed experiments, and sending emails to authors to ask for optimal tuning of parameters, etc. I did't find any existing algorithms have better performance than mine. If you find one, please let me know.

Here is a comparison among all well-known algorithms when block partition is given (signal length was fixed while we changed the measurement number; see the paper for details) :

Here is a comparison among existing algorithms when block partition is unknown (signal length, measurement number, and the number of nonzero elements in the signal were fixed while we changed the nonzero block number; each block had random size and location. See the paper for details)

Here is an experiment result showing our algorithms have better performance when intra-block correlation increases (see the paper for details)

Here is an experiment result showing the performance of Block-CoSaMP and Block-OMP is almost not affected by the intra-block correlation (see the paper for details).

**intra-block correlation**with**known**or**unknown**block partition:**Zhilin Zhang, Bhaskar D. Rao , Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation, submitted to IEEE Transaction on Signal Processing, January 2012**

The associated codes can be downloaded here:

**https://sites.google.com/site/researchbyzhang/bsbl**Here is the abstract:

We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting intra-block correlation and the other by generalizing the block structure. We propose two families of algorithms based on the framework of block sparse Bayesian learning (bSBL). One family, directly derived from the bSBL framework, requires knowledge of the block partition. Another family, derived from an expanded bSBL framework, is based on a weaker assumption about the a priori information of the block structure, and can be used in the cases when block partition, block size, block sparsity are all unknown. Using these algorithms we show that exploiting intra-block correlation is very helpful to improve recovery performance. These algorithms also shed light on how to modify existing algorithms or design new ones to exploit such correlation for improved performance.

**http://arxiv.org/abs/1201.0862**. The codes will be posted soon. But you can send emails to me for these codes right now.In this paper, we proposed three algorithms (BSBL-EM, BSBL-BO, BSBL-L1) for the block sparse model when block partition is known, and three algorithms (EBSBL-EM, EBSBL-BO, EBSBL-L1) for the model when block partition is unknown.

Here are some highlights:

**[1] These algorithms have the best recovery performance among all the existing algorithms**I have spent more than one month to read published algorithms, downloaded their codes, performed experiments, and sending emails to authors to ask for optimal tuning of parameters, etc. I did't find any existing algorithms have better performance than mine. If you find one, please let me know.

Here is a comparison among all well-known algorithms when block partition is given (signal length was fixed while we changed the measurement number; see the paper for details) :

Here is a comparison among existing algorithms when block partition is unknown (signal length, measurement number, and the number of nonzero elements in the signal were fixed while we changed the nonzero block number; each block had random size and location. See the paper for details)

**[2] These algorithms are the first algorithms that adaptively exploit intra-block correlation, i.e. the correlation among elements of a block.****[3] We revealed that intra-block correlation, if exploited, can significantly improve recovery performance and reduce the number of measurements.**Here is an experiment result showing our algorithms have better performance when intra-block correlation increases (see the paper for details)

**[4] We also found that the intra-block correlation has little effects on the performance of existing algorithms.**__This is different to our finding on the MMV model, where we found temporal correlation has obvious negative effects on the performance of existing algorithms__**(for temporal correlation on the algorithm performance, see here).**Here is an experiment result showing the performance of Block-CoSaMP and Block-OMP is almost not affected by the intra-block correlation (see the paper for details).

Subscribe to:
Posts (Atom)