Friday, October 24, 2014

Book: "Foundations of Data Science" by John Hopcroft and Ravindran Kannan

 
I just found out about a draft version of "Foundations of Data Science" a new book by John Hopcroft and Ravindran Kannan and very much like chapter 7 through 10 that touches upon themes we talk about often here on Nuit Blanche: Compressive Sensing ( see also The Big Picture in Compressive Sensing) , Advanced Matrix Factorization (see also the Advanced Matrix Factorization Jungle Page), complexity, streaming/sketching issues and Randomized Numerical Linear Algebra, Machine Learning and more. Let us note that compressive sensing is introduced at the very end, much like the other foundation book on signal processing this week (Books: "Foundations of Signal Processing" and "Fourier and Wavelet Signal Processing"). One wonders if a certain clarity would come earlier if the subject was introduced first so that titles such as Compressive Sensing Demystified ( by Frank Bucholtz and Jonathan M. Nichols) would not be needed.
 
Here is the table of content for the last chapters.
7 Algorithms for Massive Data Problems 238
7.1 Frequency Moments of Data Streams . . . . . . . . . . . . . . . . . . . . . 238
7.1.1 Number of Distinct Elements in a Data Stream . . . . . . . . . . . 239
7.1.2 Counting the Number of Occurrences of a Given Element. . . . . . 243
7.1.3 Counting Frequent Elements . . . . . . . . . . . . . . . . . . . . . . 243
7.1.4 The Second Moment . . . . . . . . . . . . . . . . . . . . . . . . . . 245
7.2 Matrix Algorithms Using Sampling . . . . . . . . . . . . . . . . . . . . . . 248
7.2.1 Matrix Multiplication Using Sampling . . . . . . . . . . . . . . . . 248
7.2.2 Sketch of a Large Matrix . . . . . . . . . . . . . . . . . . . . . . . . 250
7.3 Sketches of Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
7.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256
8 Clustering 260
8.1 Some Clustering Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
8.2 A k-means Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . 263
8.3 A Greedy Algorithm for k-Center Criterion Clustering . . . . . . . . . . . 265
8.4 Spectral Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
8.5 Recursive Clustering Based on Sparse Cuts . . . . . . . . . . . . . . . . . . 273
8.6 Kernel Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
8.7 Agglomerative Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
8.8 Dense Submatrices and Communities . . . . . . . . . . . . . . . . . . . . . 278
8.9 Flow Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
8.10 Finding a Local Cluster Without Examining the Whole Graph . . . . . . . 284
8.11 Axioms for Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289
8.11.1 An Impossibility Result . . . . . . . . . . . . . . . . . . . . . . . . 289
8.11.2 A Satis able Set of Axioms . . . . . . . . . . . . . . . . . . . . . . 295
8.12 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
9 Topic Models, Hidden Markov Process, Graphical Models, and Belief Propagation 301
9.1 Topic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
9.2 Hidden Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
9.3 Graphical Models, and Belief Propagation . . . . . . . . . . . . . . . . . . 310
9.4 Bayesian or Belief Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 311
9.5 Markov Random Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312
9.6 Factor Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
9.7 Tree Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314
9.8 Message Passing in general Graphs . . . . . . . . . . . . . . . . . . . . . . 315
9.9 Graphs with a Single Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . 317
9.10 Belief Update in Networks with a Single Loop . . . . . . . . . . . . . . . . 319
9.11 Maximum Weight Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 320
9.12 Warning Propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324
9.13 Correlation Between Variables . . . . . . . . . . . . . . . . . . . . . . . . . 325
9.14 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
 

10 Other Topics 332
10.1 Rankings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .332
10.2 Hare System for Voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . .334
10.3 Compressed Sensing and Sparse Vectors . . . . . . . . . . . . . . . . . . .335
10.3.1 Unique Reconstruction of a Sparse Vector . . . . . . . . . . . . . .336
10.3.2 The Exact Reconstruction Property . . . . . . . . . . . . . . . . . .339
10.3.3 Restricted Isometry Property . . . . . . . . . . . . . . . . . . . . .340
10.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .342
10.4.1 Sparse Vector in Some Coordinate Basis . . . . . . . . . . . . . . .342
10.4.2 A Representation Cannot be Sparse in Both Time and Frequency Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .342
10.4.3 Biological . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .345
10.4.4 Finding Overlapping Cliques or Communities . . . . . . . . . . . .345
10.4.5 Low Rank Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . .346
10.5 Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .347
10.6 Linear Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .348
10.6.1 The Ellipsoid Algorithm . . . . . . . . . . . . . . . . . . . . . . . .350
10.7 Integer Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .351
10.8 Semi-De nite Programming . . . . . . . . . . . . . . . . . . . . . . . . . .352
10.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356
 



 
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SparsePR: Robust Sparse Phase Retrieval Made Easy - implementation -


Robust Sparse Phase Retrieval Made Easy by Mark Iwen, Aditya Viswanathan, Yang Wang

In this short note we propose a simple two-stage sparse phase retrieval strategy that uses a near-optimal number of measurements, and is both computationally efficient and robust to measurement noise. In addition, the proposed strategy is fairly general, allowing for a large number of new measurement constructions and recovery algorithms to be designed with minimal effort.  
The implementation of SparsePR is here. SparsePR uses CVX and TFOCS.
 
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Thursday, October 23, 2014

Adaptive Compressed Sensing for Estimation of Structured Sparse Sets

Rui just sent me the following:
 
Hi Igor,

Not sure if this is the appropriate way to do it, but I wanted to (shamelessly) point you (and the readers of nuit blanche) to our recent work on adaptive compressive sensing for structured sparsity:


In this work we investigate the problem of estimating the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the signal strength required (and sufficient) to accurately estimate such sets through compressive measurements, while simultaneously providing adaptive support recovery protocols that perform near optimally for these classes. We show that by adaptively designing the sensing matrix we can attain significant performance gains over non-adaptive protocols. These gains arise from the fact that adaptive sensing can: (i) better mitigate the effects of noise, and (ii) better capitalize on the structure of the support sets.

Hope this is interesting to you and your readers! All the best,

Rui

--
Dept. of Mathematics and Computer Science, TU/e
http://www.win.tue.nl/~rmcastro
 
No shame to be had Rui, it's good and interesting work !
 
 
 
 
Here is the paper: Adaptive Compressed Sensing for Estimation of Structured Sparse Sets by Rui M. Castro, Ervin Tánczos

This paper investigates the problem of estimating the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the fundamental limits of accurately estimating such sets through compressive measurements, while simultaneously providing adaptive support recovery protocols that perform near optimally for these classes. We show that by adaptively designing the sensing matrix we can attain significant performance gains over non-adaptive protocols. These gains arise from the fact that adaptive sensing can: (i) better mitigate the effects of noise, and (ii) better capitalize on the structure of the support sets.
 
 
 
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Wednesday, October 22, 2014

Compressed Manifold Modes for Mesh Processing - implementation -


Kiran Varanasi mentioned it on the Google+ Community. He also said:


Compressed manifold modes are compressed eigenfunctions of the Laplace-Beltrami operator on 3D manifold surfaces. They constitute a novel functional basis, called the compressed manifold basis, where each function has local support. We derive a method, based on ADMM, for computing compressed manifold modes (CMMs) for discrete polyhedral 3D meshes. We show that CMMs identify key shape features, yielding an intuitive understanding of the basis for a human observer, where a shape can be processed as a collection of parts. We demonstrate various applications in 3D geometry processing. Our paper is published at the Symposium of Geometry Processing (SGP) 2014. We release the source-code for the method.

Please also refer to "Compressed modes for variational problems in mathematics and physics" - Ozolins, Lai, Caflisch & Osher, PNAS 2013.

Prof. Stan Osher's talk on their PNAS paper (and more) can be found here:

http://nuit-blanche.blogspot.fr/2013/08/videos-and-slides-sahd-2013-duke.html


Thanks Kiran ! Here is the paper and attendant implementation:
 
Compressed Manifold Modes for Mesh Processing by Thomas Neumann, Kiran Varanasi, Christian Theobalt, Marcus Magnor, and Markus Wacker


This paper introduces compressed eigenfunctions of the Laplace-Beltrami operator on 3D manifold surfaces. They constitute a novel functional basis, called the compressed manifold basis, where each function has local support. We derive an algorithm, based on the alternating direction method of multipliers (ADMM), to compute this basis on a given triangulated mesh. We show that compressed manifold modes identify key shape features, yielding an intuitive understanding of the basis for a human observer, where a shape can be processed as a collection of parts. We evaluate compressed manifold modes for potential applications in in shape matching and mesh abstraction. Our results show that this basis has distinct advantages over existing alternatives, indicating high potential for a wide range of use-cases in mesh processing.
The project page with implementation is here.
 
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Tuesday, October 21, 2014

Books: "Foundations of Signal Processing" and "Fourier and Wavelet Signal Processing"

Looks like Martin Vetterli, Jelena Kovacevic and Vivek Goyal went through the monumental undertaking of writing a book on the foundations of signal processing aptly titled Foundations of Signal Processing. You can get it directly from Cambridge University Press, from Amazon, or from another retailer.


Let us note that a trimmed down version is available in pdf from the book's website:


 
and let us also note that the book has a companion book called Fourier and Wavelet Signal Processing that is not yet finished. That second book gets to compressive sensing at the very end: a situation not unlike that of certain last sentences in Isaac Asimov's Foundation series.


 
PS: Until I looked it up, I did not know that Asimov had been a professor at Boston University where
Vivek, one of the author, also teaches.
 
 
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On Sparse Representation in Fourier and Local Bases - implementation -

Following up on this entry "On Sparse Representation in Fourier and Local Bases", Pier Luigi followed up with an implementation of ProSparse and the code that produces the figures in that article:  
Dear Igor,

thanks for the prompt answer.

In attachment [ http://www.commsp.ee.ic.ac.uk/~pld/software/ ] you'll find the MATLAB files to reproduce Fig2 and Fig3 of the paper.

Note that you still need to have the CVX package installed in your Matlab distribution (http://cvxr.com/cvx/) to run the files.

You have to execute the script “show_counter_examples.m”. There is a variable that controls which result to reproduce:
- line 9 @ show_counter_examples.m: IsMultipleSolution = 0; ====> reproduce figure 2
- line 9 @ show_counter_examples.m: IsMultipleSolution = 1; ====> reproduce figure 3


Please, note that these routines have been written for the sole purpose of reproducing the results in the figures and should not be treated as routines that can be easily adapted to alternative settings.

The zip file is also available at the following link
http://www.commsp.ee.ic.ac.uk/~pld/software/


all the best,
Pier Luigi
Pier Luigi was kind enough to provide a deep link but it is my experience that a better policy is to direct interested parties to a nice landing page which in this case is his software page.
 
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Monday, October 20, 2014

Thesis: Approximate Message Passing Algorithms for Generalized Bilinear Inference, Jason Parker


Here is a new thesis: Approximate Message Passing Algorithms for Generalized Bilinear Inference by Jason Parker

Recent developments in compressive sensing (CS) combined with increasing demands for effective high-dimensional inference techniques across a variety of disciplines have motivated extensive research into algorithms exploiting various notions of parsimony, including sparsity and low-rank constraints. In this dissertation, we extend the generalized approximate message passing (GAMP) approach, originally proposed for high-dimensional generalized-linear regression in the context of CS, to handle several classes of bilinear inference problems. First, we consider a general form of noisy CS where there is uncertainty in the measurement matrix as well as in the measurements. Matrix uncertainty is motivated by practical cases in which there are imperfections or unknown calibration parameters in the signal acquisition hardware. While previous work has focused on analyzing and extending classical CS algorithms like the LASSO and Dantzig selector for this problem setting, we propose a new algorithm called Matrix Uncertain GAMP (MU-GAMP) whose goal is minimization of mean-squared error of the signal estimates in the presence of these uncertainties, without attempting to estimate the uncertain measurement matrix itself. Next, we extend GAMP to the generalized-bilinear case, in which the measurement matrix is estimated jointly with the signals of interest, enabling its application to matrix completion, robust PCA, dictionary learning, and related matrix-factorization problems. We derive this Bilinear GAMP (BiG-AMP) algorithm as an approximation of the sum-product belief propagation algorithm in the high-dimensional limit, where central limit theorem arguments and Taylor-series approximations apply, and under the assumption of statistically independent matrix entries with known priors. In addition, we propose an adaptive damping mechanism that aids convergence under finite problem sizes, an expectation-maximization (EM)-based method to automatically tune the parameters of the assumed priors, and two rank-selection strategies. We then discuss the specializations of EM-BiG-AMP to the problems of matrix completion, robust PCA, and dictionary learning, and present the results of an extensive empirical study comparing EM-BiG-AMP to state-of-the-art algorithms on each problem. Our numerical results, using both synthetic and real-world datasets, demonstrate that EM-BiG-AMP yields excellent reconstruction accuracy (often best in class) while maintaining competitive runtimes and avoiding the need to tune algorithmic parameters. Finally, we propose a parametric extension known as P-BiG-AMP, which recovers BiG-AMP as a special case, that relaxes the assumption of statistically independent matrix entries by introducing parametric models for the two matrix factors. The resulting algorithm is rigorously justified for random affine parameterizations and constructed to allow its use with an even wider class of non-linear parameterizations, enabling numerous potential applications.

Jason is one of the Map Makers.
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Sunday, October 19, 2014

Watching Closely: Asteroids, Comets



Twenty years ago, we watched an asteroid hit Jupiter with mostly Earth or orbital based telescopes. Today, we will have a comet past Mars and it will be watched by our robots on Mars, in less than a month, we will be harpooning another comet on a spot that needs a name, while Hubble continues to downselect potential asteroid to visit by one of our spacecraft in the Kuiper belt. We live in interesting times.


Watch live streaming video from eurospaceagency at livestream.com

More in-depth discussions can be found in the videos from "The Comet Siding Spring and Its Close Approach to Mars Observer's Workshop" (4 Sessions).
The Comet Siding Spring and Its Close Approach to Mars Observer's Workshop:


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Sunday Morning Insight: Crossing into P territory

 



To recap, in compressive sensing, it's been known for a while that some solutions can be found thanks to l_1 (P or Polynomial time) relaxation of combinatorial problems (NP). In fact, the whole field of compressive sensing took off when people realized one could be on the P side most of the time.

In genome sequencing the latest long read technology have enabled the whole field to transport itself  from an NP territory into one where polynomial-time algorithms (P) will do OK. The threshold to cross is about 2K. Here is what we can read from the PacBio technology



When you go in P territory, many things change, here is one:

and here is what people say about the Oxford Nanopore technology.
 
 
 
 
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Friday, October 17, 2014

On Sparse Representation in Fourier and Local Bases

I just received the following from Pier Luigi:
Dear Igor,

I hope things are good with you and sorry for this email out of the blue.

I am writing to you because together with Yue Lu from Harvard University we have revisited the classical
problem of finding the sparse representation of a signal in a pair of bases, specifically Fourier and local bases,
and introduced a polynomial complexity algotithm, ProSparse, that solves the problem under the unicity constraint only. Thus the algorithm
works also when l_1 fails. In this way we implicitly demonstrate that the sparse representation problem for these specific structured dictionaries is not NP hard.

This is a result that applies, currently, to a limited number of cases, but given that it looks at the sparse representation problem in a way different from the traditional l_1 approach, I thought that this may
intrigue the nuit-blanche community. And this is why I'm contacting you. If you think it is worth it, you may consider mentioning it
in your blog.

The paper is open-access and is available at:


all the best,
Pier Luigi
Thanks Pier Luigi ! Here is the paper:
 
 
 
 
We consider the classical problem of finding the sparse representation of a signal in a pair of bases. When both bases are orthogonal, it is known that the sparse representation is unique when the sparsity K of the signal satisfies K lt 1/μ(D), where μ(D) is the mutual coherence of the dictionary. Furthermore, the sparse representation can be obtained in polynomial time by Basis Pursuit (BP), when K lt 0.91/μ(D). Therefore, there is a gap between the unicity condition and the one required to use the polynomial-complexity BP formulation. For the case of general dictionaries, it is also well known that finding the sparse representation under the only constraint of unicity is NP-hard. In this paper, we introduce, for the case of Fourier and canonical bases, a polynomial complexity algorithm that finds all the possible K-sparse representations of a signal under the weaker condition that K lt √2/μ(D). Consequently, when K lt 1/μ(D), the proposed algorithm solves the unique sparse representation problem for this structured dictionary in polynomial time. We further show that the same method can be extended to many other pairs of bases, one of which must have local atoms.Examples include the union of Fourier and local Fourier bases, the union of discrete cosine transform and canonical bases, and the union of random Gaussian and canonical bases.
An implementation can be found here.
 
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