May 5, 2019

3016 words 15 mins read

Paper Group ANR 520

Kernel Alignment for Unsupervised Transfer Learning. How do people explore virtual environments?. Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel. Viral Search algorithm. Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets. Holistic Features For Real-Time Crowd Behaviour …

Kernel Alignment for Unsupervised Transfer Learning


Title	Kernel Alignment for Unsupervised Transfer Learning
Authors	Ievgen Redko, Younès Bennani
Abstract	The ability of a human being to extrapolate previously gained knowledge to other domains inspired a new family of methods in machine learning called transfer learning. Transfer learning is often based on the assumption that objects in both target and source domains share some common feature and/or data space. In this paper, we propose a simple and intuitive approach that minimizes iteratively the distance between source and target task distributions by optimizing the kernel target alignment (KTA). We show that this procedure is suitable for transfer learning by relating it to Hilbert-Schmidt Independence Criterion (HSIC) and Quadratic Mutual Information (QMI) maximization. We run our method on benchmark computer vision data sets and show that it can outperform some state-of-art methods.
Tasks	Transfer Learning
Published	2016-10-20
URL	http://arxiv.org/abs/1610.06434v1
PDF	http://arxiv.org/pdf/1610.06434v1.pdf
PWC	https://paperswithcode.com/paper/kernel-alignment-for-unsupervised-transfer
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How do people explore virtual environments?


Title	How do people explore virtual environments?
Authors	Vincent Sitzmann, Ana Serrano, Amy Pavel, Maneesh Agrawala, Diego Gutierrez, Belen Masia, Gordon Wetzstein
Abstract	Understanding how people explore immersive virtual environments is crucial for many applications, such as designing virtual reality (VR) content, developing new compression algorithms, or learning computational models of saliency or visual attention. Whereas a body of recent work has focused on modeling saliency in desktop viewing conditions, VR is very different from these conditions in that viewing behavior is governed by stereoscopic vision and by the complex interaction of head orientation, gaze, and other kinematic constraints. To further our understanding of viewing behavior and saliency in VR, we capture and analyze gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions. We provide a thorough analysis of our data, which leads to several important insights, such as the existence of a particular fixation bias, which we then use to adapt existing saliency predictors to immersive VR conditions. In addition, we explore other applications of our data and analysis, including automatic alignment of VR video cuts, panorama thumbnails, panorama video synopsis, and saliency-based compression.
Tasks
Published	2016-12-13
URL	http://arxiv.org/abs/1612.04335v2
PDF	http://arxiv.org/pdf/1612.04335v2.pdf
PWC	https://paperswithcode.com/paper/how-do-people-explore-virtual-environments
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Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel


Title	Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel
Authors	Ali Burak Ünal, Öznur Taştan
Abstract	Characterizing patient somatic mutations through next-generation sequencing technologies opens up possibilities for refining cancer subtypes. However, catalogues of mutations reveal that only a small fraction of the genes are altered frequently in patients. On the other hand different genomic alterations may perturb the same pathways. We propose a novel clustering procedure that quantifies the similarities of patients from their mutational profile on pathways via a novel graph kernel. We represent each KEGG pathway as an undirected graph. For each patient the vertex labels are assigned based on her altered genes. Smoothed shortest path graph kernel (smSPK) evaluates each pair of patients by comparing their vertex labeled pathway graphs. Our clustering procedure involves two steps: the smSPK kernel matrix derived for each pathway are input to kernel k-means algorithm and each pathway is evaluated individually. In the next step, only those pathways that are successful are combined in to a single kernel input to kernel k-means to stratify patients. Evaluating the procedure on simulated data showed that smSPK clusters patients up to 88% accuracy. Finally to identify ovarian cancer patient subgroups, we apply our methodology to the cancer genome atlas ovarian data that involves 481 patients. The identified subgroups are evaluated through survival analysis. Grouping patients into four clusters results with patients groups that are significantly different in their survival times ($p$-value $\le 0.005$).
Tasks	Survival Analysis
Published	2016-12-13
URL	http://arxiv.org/abs/1612.04431v2
PDF	http://arxiv.org/pdf/1612.04431v2.pdf
PWC	https://paperswithcode.com/paper/identification-of-cancer-patient-subgroups
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Viral Search algorithm


Title	Viral Search algorithm
Authors	Matteo Gardini
Abstract	The article, after a brief introduction on genetic algorithms and their functioning, presents a kind of genetic algorithm called Viral Search. We present the key concepts, we formally derive the algorithm and we perform numerical tests designed to illustrate the potential and limits.
Tasks
Published	2016-06-14
URL	http://arxiv.org/abs/1606.04306v1
PDF	http://arxiv.org/pdf/1606.04306v1.pdf
PWC	https://paperswithcode.com/paper/viral-search-algorithm
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Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets


Title	Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets
Authors	Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos
Abstract	The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning ‘mind from the machine’ in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of predictive accuracy and that multiple, equally predictive signatures are actually present in real world data.
Tasks	Feature Selection, Survival Analysis
Published	2016-11-10
URL	http://arxiv.org/abs/1611.03227v1
PDF	http://arxiv.org/pdf/1611.03227v1.pdf
PWC	https://paperswithcode.com/paper/feature-selection-with-the-r-package-mxm
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Holistic Features For Real-Time Crowd Behaviour Anomaly Detection


Title	Holistic Features For Real-Time Crowd Behaviour Anomaly Detection
Authors	M. Marsden, K. McGuinness, S. Little, N. E. O’Connor
Abstract	This paper presents a new approach to crowd behaviour anomaly detection that uses a set of efficiently computed, easily interpretable, scene-level holistic features. This low-dimensional descriptor combines two features from the literature: crowd collectiveness [1] and crowd conflict [2], with two newly developed crowd features: mean motion speed and a new formulation of crowd density. Two different anomaly detection approaches are investigated using these features. When only normal training data is available we use a Gaussian Mixture Model (GMM) for outlier detection. When both normal and abnormal training data is available we use a Support Vector Machine (SVM) for binary classification. We evaluate on two crowd behaviour anomaly detection datasets, achieving both state-of-the-art classification performance on the violent-flows dataset [3] as well as better than real-time processing performance (40 frames per second).
Tasks	Anomaly Detection, Outlier Detection
Published	2016-06-16
URL	http://arxiv.org/abs/1606.05310v1
PDF	http://arxiv.org/pdf/1606.05310v1.pdf
PWC	https://paperswithcode.com/paper/holistic-features-for-real-time-crowd
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Sparse Signal Recovery for Binary Compressed Sensing by Majority Voting Neural Networks


Title	Sparse Signal Recovery for Binary Compressed Sensing by Majority Voting Neural Networks
Authors	Daisuke Ito, Tadashi Wadayama
Abstract	In this paper, we propose majority voting neural networks for sparse signal recovery in binary compressed sensing. The majority voting neural network is composed of several independently trained feedforward neural networks employing the sigmoid function as an activation function. Our empirical study shows that a choice of a loss function used in training processes for the network is of prime importance. We found a loss function suitable for sparse signal recovery, which includes a cross entropy-like term and an $L_1$ regularized term. From the experimental results, we observed that the majority voting neural network achieves excellent recovery performance, which is approaching the optimal performance as the number of component nets grows. The simple architecture of the majority voting neural networks would be beneficial for both software and hardware implementations.
Tasks
Published	2016-10-29
URL	http://arxiv.org/abs/1610.09463v1
PDF	http://arxiv.org/pdf/1610.09463v1.pdf
PWC	https://paperswithcode.com/paper/sparse-signal-recovery-for-binary-compressed
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Conversational flow in Oxford-style debates


Title	Conversational flow in Oxford-style debates
Authors	Justine Zhang, Ravi Kumar, Sujith Ravi, Cristian Danescu-Niculescu-Mizil
Abstract	Public debates are a common platform for presenting and juxtaposing diverging views on important issues. In this work we propose a methodology for tracking how ideas flow between participants throughout a debate. We use this approach in a case study of Oxford-style debates—a competitive format where the winner is determined by audience votes—and show how the outcome of a debate depends on aspects of conversational flow. In particular, we find that winners tend to make better use of a debate’s interactive component than losers, by actively pursuing their opponents’ points rather than promoting their own ideas over the course of the conversation.
Tasks
Published	2016-04-11
URL	http://arxiv.org/abs/1604.03114v1
PDF	http://arxiv.org/pdf/1604.03114v1.pdf
PWC	https://paperswithcode.com/paper/conversational-flow-in-oxford-style-debates
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Private Topic Modeling


Title	Private Topic Modeling
Authors	Mijung Park, James Foulds, Kamalika Chaudhuri, Max Welling
Abstract	We develop a privatised stochastic variational inference method for Latent Dirichlet Allocation (LDA). The iterative nature of stochastic variational inference presents challenges: multiple iterations are required to obtain accurate posterior distributions, yet each iteration increases the amount of noise that must be added to achieve a reasonable degree of privacy. We propose a practical algorithm that overcomes this challenge by combining: (1) an improved composition method for differential privacy, called the moments accountant, which provides a tight bound on the privacy cost of multiple variational inference iterations and thus significantly decreases the amount of additive noise; and (2) privacy amplification resulting from subsampling of large-scale data. Focusing on conjugate exponential family models, in our private variational inference, all the posterior distributions will be privatised by simply perturbing expected sufficient statistics. Using Wikipedia data, we illustrate the effectiveness of our algorithm for large-scale data.
Tasks
Published	2016-09-14
URL	http://arxiv.org/abs/1609.04120v3
PDF	http://arxiv.org/pdf/1609.04120v3.pdf
PWC	https://paperswithcode.com/paper/private-topic-modeling
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Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences


Title	Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences
Authors	Wenbin Li, Darren Cosker, Matthew Brown
Abstract	It is hard to densely track a nonrigid object in long term, which is a fundamental research issue in the computer vision community. This task often relies on estimating pairwise correspondences between images over time where the error is accumulated and leads to a drift issue. In this paper, we introduce a novel optimization framework with an Anchor Patch constraint. It is supposed to significantly reduce overall errors given long sequences containing non-rigidly deformable objects. Our framework can be applied to any dense tracking algorithm, e.g. optical flow. We demonstrate the success of our approach by showing significant error reduction on 6 popular optical flow algorithms applied to a range of real-world nonrigid benchmarks. We also provide quantitative analysis of our approach given synthetic occlusions and image noise.
Tasks	Optical Flow Estimation
Published	2016-03-07
URL	http://arxiv.org/abs/1603.02252v1
PDF	http://arxiv.org/pdf/1603.02252v1.pdf
PWC	https://paperswithcode.com/paper/drift-robust-non-rigid-optical-flow
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Classification of Large-Scale Fundus Image Data Sets: A Cloud-Computing Framework


Title	Classification of Large-Scale Fundus Image Data Sets: A Cloud-Computing Framework
Authors	Sohini Roychowdhury
Abstract	Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.
Tasks
Published	2016-03-26
URL	http://arxiv.org/abs/1603.08071v1
PDF	http://arxiv.org/pdf/1603.08071v1.pdf
PWC	https://paperswithcode.com/paper/classification-of-large-scale-fundus-image
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How to Train Your Deep Neural Network with Dictionary Learning


Title	How to Train Your Deep Neural Network with Dictionary Learning
Authors	Vanika Singhal, Shikha Singh, Angshul Majumdar
Abstract	Currently there are two predominant ways to train deep neural networks. The first one uses restricted Boltzmann machine (RBM) and the second one autoencoders. RBMs are stacked in layers to form deep belief network (DBN); the final representation layer is attached to the target to complete the deep neural network. Autoencoders are nested one inside the other to form stacked autoencoders; once the stcaked autoencoder is learnt the decoder portion is detached and the target attached to the deepest layer of the encoder to form the deep neural network. This work proposes a new approach to train deep neural networks using dictionary learning as the basic building block; the idea is to use the features from the shallower layer as inputs for training the next deeper layer. One can use any type of dictionary learning (unsupervised, supervised, discriminative etc.) as basic units till the pre-final layer. In the final layer one needs to use the label consistent dictionary learning formulation for classification. We compare our proposed framework with existing state-of-the-art deep learning techniques on benchmark problems; we are always within the top 10 results. In actual problems of age and gender classification, we are better than the best known techniques.
Tasks	Age And Gender Classification, Dictionary Learning
Published	2016-12-22
URL	http://arxiv.org/abs/1612.07454v1
PDF	http://arxiv.org/pdf/1612.07454v1.pdf
PWC	https://paperswithcode.com/paper/how-to-train-your-deep-neural-network-with
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How Transferable are CNN-based Features for Age and Gender Classification?


Title	How Transferable are CNN-based Features for Age and Gender Classification?
Authors	Gökhan Özbulak, Yusuf Aytar, Hazım Kemal Ekenel
Abstract	Age and gender are complementary soft biometric traits for face recognition. Successful estimation of age and gender from facial images taken under real-world conditions can contribute improving the identification results in the wild. In this study, in order to achieve robust age and gender classification in the wild, we have benefited from Deep Convolutional Neural Networks based representation. We have explored transferability of existing deep convolutional neural network (CNN) models for age and gender classification. The generic AlexNet-like architecture and domain specific VGG-Face CNN model are employed and fine-tuned with the Adience dataset prepared for age and gender classification in uncontrolled environments. In addition, task specific GilNet CNN model has also been utilized and used as a baseline method in order to compare with transferred models. Experimental results show that both transferred deep CNN models outperform the GilNet CNN model, which is the state-of-the-art age and gender classification approach on the Adience dataset, by an absolute increase of 7% and 4.5% in accuracy, respectively. This outcome indicates that transferring a deep CNN model can provide better classification performance than a task specific CNN model, which has a limited number of layers and trained from scratch using a limited amount of data as in the case of GilNet. Domain specific VGG-Face CNN model has been found to be more useful and provided better performance for both age and gender classification tasks, when compared with generic AlexNet-like model, which shows that transfering from a closer domain is more useful.
Tasks	Age And Gender Classification, Face Recognition
Published	2016-10-01
URL	http://arxiv.org/abs/1610.00134v1
PDF	http://arxiv.org/pdf/1610.00134v1.pdf
PWC	https://paperswithcode.com/paper/how-transferable-are-cnn-based-features-for
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Generalized Wishart processes for interpolation over diffusion tensor fields


Title	Generalized Wishart processes for interpolation over diffusion tensor fields
Authors	Hernan Dario Vargas Cardona, Mauricio A. Alvarez, Alvaro A. Orozco
Abstract	Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive tool for watching the microstructure of fibrous nerve and muscle tissue. From dMRI, it is possible to estimate 2-rank diffusion tensors imaging (DTI) fields, that are widely used in clinical applications: tissue segmentation, fiber tractography, brain atlas construction, brain conductivity models, among others. Due to hardware limitations of MRI scanners, DTI has the difficult compromise between spatial resolution and signal noise ratio (SNR) during acquisition. For this reason, the data are often acquired with very low resolution. To enhance DTI data resolution, interpolation provides an interesting software solution. The aim of this work is to develop a methodology for DTI interpolation that enhance the spatial resolution of DTI fields. We assume that a DTI field follows a recently introduced stochastic process known as a generalized Wishart process (GWP), which we use as a prior over the diffusion tensor field. For posterior inference, we use Markov Chain Monte Carlo methods. We perform experiments in toy and real data. Results of GWP outperform other methods in the literature, when compared in different validation protocols.
Tasks
Published	2016-06-25
URL	http://arxiv.org/abs/1606.07968v1
PDF	http://arxiv.org/pdf/1606.07968v1.pdf
PWC	https://paperswithcode.com/paper/generalized-wishart-processes-for
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A Bayesian optimization approach to find Nash equilibria


Title	A Bayesian optimization approach to find Nash equilibria
Authors	Victor Picheny, Mickael Binois, Abderrahmane Habbal
Abstract	Game theory finds nowadays a broad range of applications in engineering and machine learning. However, in a derivative-free, expensive black-box context, very few algorithmic solutions are available to find game equilibria. Here, we propose a novel Gaussian-process based approach for solving games in this context. We follow a classical Bayesian optimization framework, with sequential sampling decisions based on acquisition functions. Two strategies are proposed, based either on the probability of achieving equilibrium or on the Stepwise Uncertainty Reduction paradigm. Practical and numerical aspects are discussed in order to enhance the scalability and reduce computation time. Our approach is evaluated on several synthetic game problems with varying number of players and decision space dimensions. We show that equilibria can be found reliably for a fraction of the cost (in terms of black-box evaluations) compared to classical, derivative-based algorithms. The method is available in the R package GPGame available on CRAN at https://cran.r-project.org/package=GPGame.
Tasks
Published	2016-11-08
URL	http://arxiv.org/abs/1611.02440v2
PDF	http://arxiv.org/pdf/1611.02440v2.pdf
PWC	https://paperswithcode.com/paper/a-bayesian-optimization-approach-to-find-nash
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