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Monday, August 31, 2015

Nuit Blanche in Review ( August 2015 )

Since the last Nuit Blanche in Review ( July 2015 ) we saw the birth of GitXiv and a few implementations.

 We also had some in-depth studies, with in particular some on-going clues of The Great Convergence in action as well as some compressive sensing hardware:
Jobs:
Theses:
Videos/Slides/proceedings

Credit: NASA/Johns Hopkins University Applied Physics Laboratory/Southwest Research Institute

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Unsupervised Feature Selection on Data Streams / Streaming Anomaly Detection Using Randomized Matrix Sketching

Today, we see the use of streaming algorithms to figure out anomaly detection or unsupervised feature selection.


Streaming Anomaly Detection Using Randomized Matrix Sketching by Hao Huang, Shiva Kasiviswanathan.
Data is continuously being generated from sources such as machines, network traffic, application logs, etc. Timely and accurate detection of anomalies in massive data streams have important applications such as in preventing machine failures, intrusion detection, and dynamic load balancing. In this paper, we introduce a novel anomaly detection algorithm, which can detect anomalies in a streaming fashion by making only one pass over the data while utilizing limited storage. The algorithm uses ideas from matrix sketching and randomized low-rank matrix approximations to maintain, in a streaming model, a set of few orthogonal vectors that form a good approximate basis for the data. Using this constructed orthogonal basis, anomalies in new incoming data are detected based on a simple reconstruction error test. We theoretically prove that our algorithm compares favorably with an offline approach based on global singular value decomposition updates. The experimental results show the effectiveness and efficiency of our approach over other popular fast anomaly detection methods.

Massive data streams are continuously being generated from sources such as social media, broadcast news, etc., and typically these datapoints lie in high-dimensional spaces (such as the vocabulary space of a language). Timely and accurate feature subset selection in these massive data streams has important applications in model interpretation, computational/storage cost reduction, and generalization enhancement. In this paper, we introduce a novel unsupervised feature selection approach on data streams that selects important features by making only one pass over the data while utilizing limited storage. The proposed algorithm uses ideas from matrix sketching to e ciently maintain a low-rank approximation of the observed data and applies regularized regression on this approximation to identify the important features. We theoretically prove that our algorithm is close to an expensive offline approach based on global singular value decompositions. The experimental results on a variety of text and image datasets demonstrate the excellent ability of our approach to identify important features even in presence of concept drifts and also its efficiency over other popular scalable feature selection algorithms.

Previously:


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Friday, August 28, 2015

Machine Learning and Homomorphic Encryption - implementation -

We have mentioned homomorphic encryption here on Nuit Blanche mostly because of Andrew McGregor et al's work on the subject (see references below). Today, we have a Machine Learning approach using this encoding strategy, which in effect is not really that far from the idea of homomorphic sketches  or random projections for low dimensional manifolds. Without further ado:


A review of homomorphic encryption and software tools for encrypted statistical machine learning by  Louis J. M. Aslett, Pedro M. Esperança, Chris C. Holmes

Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent limitations inherent in the current state of the art. These limitations restrict the kind of statistics and machine learning algorithms which can be implemented and we review those which have been successfully applied in the literature. Finally, we document a high performance R package implementing a recent homomorphic scheme in a general framework.


Encrypted statistical machine learning: new privacy preserving methods  by Louis J. M. Aslett, Pedro M. Esperança, Chris C. Holmes

We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving statistical machine learning analysis and modelling of encrypted data without compromising security constraints. We propose tailored algorithms for applying extremely random forests, involving a new cryptographic stochastic fraction estimator, and na\"{i}ve Bayes, involving a semi-parametric model for the class decision boundary, and show how they can be used to learn and predict from encrypted data. We demonstrate that these techniques perform competitively on a variety of classification data sets and provide detailed information about the computational practicalities of these and other FHE methods.
 An implementation in R is available here:
 References:
  h/t Albert Swart
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Mash: Fast genome distance estimation using the MinHash algorithm - implementation -




In this Sunday Morning Insight: What Happens When You Cross into P Territory ?, I mentioned this article on using LSH for genome alignment from long read technology (PacBio RS II or Oxford Nanopore MiNion).

Assembling Large Genomes with Single-Molecule Sequencing and Locality Sensitive Hashing by Konstantin Berlin, Sergey Koren, Chen-Shan Chin, James Drake, Jane M Landolin, Adam M Phillippy

while assembling the genome is important, with cheap and fast long reads, the goalpost is now slowly moving to the unsupervised learning of groups of genomes. That type of unsupervised learning can only be enabled with the right dimensionality reduction technique, today it is MinHash

Here is Mash: Fast genome distance estimation using the MinHash algorithm from Adam Phillippy's group.



Wonder how MinHash works, check this write-up by Matthew Casperson on MinHash for dummies.

Related:

      
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    Thursday, August 27, 2015

    Toward a unified theory of sparse dimensionality reduction in Euclidean space

     So we are now converging toward a theory for using sparse random projections for a variety of problems.
     
    Toward a unified theory of sparse dimensionality reduction in Euclidean space  by Jean Bourgain, Sjoerd Dirksen, Jelani Nelson

    Let ΦRm×n be a sparse Johnson-Lindenstrauss transform [KN14] with s non-zeroes per column. For a subset T of the unit sphere, ε(0,1/2) given, we study settings for m,s required to ensure
    EΦsupxTΦx221<ε,
    i.e. so that Φ preserves the norm of every xT simultaneously and multiplicatively up to 1+ε. We introduce a new complexity parameter, which depends on the geometry of T, and show that it suffices to choose s and m such that this parameter is small. Our result is a sparse analog of Gordon's theorem, which was concerned with a dense Φ having i.i.d. Gaussian entries. We qualitatively unify several results related to the Johnson-Lindenstrauss lemma, subspace embeddings, and Fourier-based restricted isometries. Our work also implies new results in using the sparse Johnson-Lindenstrauss transform in numerical linear algebra, classical and model-based compressed sensing, manifold learning, and constrained least squares problems such as the Lasso.
     
    h/t Laurent Jacques
     
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    Comparative Analysis of Scattering and Random Features in Hyperspectral Image Classification

    The great convergence is happenng right under our eyes. Here is a comparison between Random Features and Scattering Transform features for classification of hyperspectral datasets:

    Hyperspectral images (HSI) contains extremely rich spectral and spatial information that offers great potential to discriminate between various land cover classes. The inherent high dimensionality and insufficient training samples in such images introduces Hughes phenomenon. In order to deal with this issue, several preprocessing techniques have been integrated in processing chain of HSI prior to classification. Supervised feature extraction is one such method which mitigates the curse of dimensionality induced by Hughes effect. In recent years, new strategies for feature extraction based on scattering transform and Random Kitchen Sink have been introduced, which can be used in context of hyperspectral image classification. This paper presents a comparative analysis of scattering and random features in hyperspectral image classification. The classification is performed using simple linear classifier such as Regularized Least Square (RLS) accessed through Grand Unified Regularized Least Squares (GURLS) library. The proposed approach is tested on two standard hyperspectral datasets namely, Salinas-A and Indian Pines subset scene captured by NASAs AVIRIS sensor (Airborne Visible Infrared Imaging Spectrometer). In order to show the effectiveness of proposed method, a comparative analysis is performed based on feature dimension, classification accuracy measures and computational time. From the comparative assessment, it is evident that classification using random features achieve excellent classification results with less computation time when compared with raw pixels(without feature extraction) and scattering features for both the datasets.
     
     
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    Wednesday, August 26, 2015

    Slides: Beating the Perils of Non-Convexity: Training Neural Networks using Tensor Methods


     
     
     
     
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    Thesis: Message Passing and Combinatorial Optimization, Siamak Ravanbakhsh


    Message Passing and Combinatorial Optimization by Siamak Ravanbakhsh

    Graphical models use the intuitive and well-studied methods of graph theory to implicitly represent dependencies between variables in large systems. They can model the global behaviour of a complex system by specifying only local factors. This thesis studies inference in discrete graphical models from an algebraic perspective and the ways inference can be used to express and approximate NP-hard combinatorial problems.
    We investigate the complexity and reducibility of various inference problems, in part by organizing them in an inference hierarchy. We then investigate tractable approximations for a subset of these problems using distributive law in the form of message passing. The quality of the resulting message passing procedure, called Belief Propagation (BP), depends on the influence of loops in the graphical model. We contribute to three classes of approximations that improve BP for loopy graphs A) loop correction techniques; B) survey propagation, another message passing technique that surpasses BP in some settings; and C) hybrid methods that interpolate between deterministic message passing and Markov Chain Monte Carlo inference.
    We then review the existing message passing solutions and provide novel graphical models and inference techniques for combinatorial problems under three broad classes: A) constraint satisfaction problems such as satisfiability, coloring, packing, set / clique-cover and dominating / independent set and their optimization counterparts; B) clustering problems such as hierarchical clustering, K-median, K-clustering, K-center and modularity optimization; C) problems over permutations including assignment, graph morphisms and alignment, finding symmetries and traveling salesman problem. In many cases we show that message passing is able to find solutions that are either near optimal or favourably compare with today's state-of-the-art approaches.
     
     
     
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    Tuesday, August 25, 2015

    Distributed Compressive Sensing: A Deep Learning Approach

    Much like last week's "Deep Learning Approach to Structured Signal Recovery", we are beginning to see some folks who use deep learning as a way of crafting reconstruction solvers. In the case presented below, I am not sure they are solving a true MMV problem as they seem to solve an even tougher problem (elements have similar sparsity as opposed to exactly the same sparsity pattern). Way to go !
     

    Distributed Compressive Sensing: A Deep Learning Approach by Hamid Palangi, Rabab Ward, Li Deng
    We address the problem of compressed sensing with Multiple Measurement Vectors (MMVs) when the structure of sparse vectors in different channels depend on each other. "The sparse vectors are not necessarily joint sparse". We capture this dependency by computing the conditional probability of each entry of each sparse vector to be non-zero given "residuals" of all previous sparse vectors. To compute these probabilities, we propose to use Long Short-Term Memory (LSTM) [1], a bottom up data driven model for sequence modelling. To compute model parameters we minimize a cross entropy cost function. We propose a greedy solver that uses above probabilities at the decoder. By performing extensive experiments on two real world datasets, we show that the proposed method significantly outperforms general MMV solver Simultaneous Orthogonal Matching Pursuit (SOMP) and model based Bayesian methods including Multitask Compressive Sensing [2] and Sparse Bayesian Learning for Temporally Correlated Sources [3]. Nevertheless, we emphasize that the proposed method is a data driven method where availability of training data is important. However, in many applications, train data is indeed available, e.g., recorded images or video.

    Related:

     
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    Monday, August 24, 2015

    Random Noise in the GoogleLeNet Architecture

    Back in 1996, Sparse coding made a big splash in Science and Engineering because the elements of a dictionary looked very much like the wavelet functions that had been discovered a few years earlier. For the first time, there was a sense that an algorithm could produce some simple insight on how the machinery of the visual cortex. 

    Roelof Pieters investigates, in his spare time, how random noise applied to different layers of current deep neural architectures produces different types of imagery. In the past few month, this process has been called DeepDreaming and has produced a few dog pictures. It is fascinating because as Pierre Sermanet speculated yesterday, some of this imagery might constitute a good detection clue for degenerative disease.

    All that is well, but yesterday, Roelof mentioned that applying random noise on a particular GoogleLeNet layer (2012) produced something else than dogs. Here are a few examples of what he calls "Limited deep dreaming" that starts with random noise activating single units from a particular layer of GoogLeNet (3a output) -all the other images are listed in this Flickr album -





    They certainly look very natural to me: Sometimes they look like structures found in electron microscopy, sometimes, they look like the structures found in numerical simulation of our universe:



     
     
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