Paper Group ANR 310
A pragmatic approach to multi-class classification. Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach. Riemannian Tensor Completion with Side Information. Personalization Effect on Emotion Recognition from Physiological Data: An Investigation of Performance on Different Setups and Classifiers. E …
A pragmatic approach to multi-class classification
| Title | A pragmatic approach to multi-class classification |
| Authors | Thomas Kopinski, Stéphane Magand, Uwe Handmann, Alexander Gepperth |
| Abstract | We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier’s output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task. |
| Tasks | Gesture Recognition |
| Published | 2016-01-06 |
| URL | http://arxiv.org/abs/1601.01121v1 |
| http://arxiv.org/pdf/1601.01121v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-pragmatic-approach-to-multi-class |
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Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach
| Title | Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach |
| Authors | Collins Leke, Tshilidzi Marwala |
| Abstract | In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as theArbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset. The investigated methodology in this paper therefore has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed. |
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| Published | 2016-07-01 |
| URL | http://arxiv.org/abs/1607.00136v1 |
| http://arxiv.org/pdf/1607.00136v1.pdf | |
| PWC | https://paperswithcode.com/paper/missing-data-estimation-in-high-dimensional |
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Riemannian Tensor Completion with Side Information
| Title | Riemannian Tensor Completion with Side Information |
| Authors | Tengfei Zhou, Hui Qian, Zebang Shen, Congfu Xu |
| Abstract | By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion problems. However, existing methods fail to exploit the easily accessible side information, due to their format mismatch. Consequently, there is still room for improvement in such methods. To fill the gap, in this paper, a novel Riemannian model is proposed to organically integrate the original model and the side information by overcoming their inconsistency. For this particular model, an efficient Riemannian conjugate gradient descent solver is devised based on a new metric that captures the curvature of the objective.Numerical experiments suggest that our solver is more accurate than the state-of-the-art without compromising the efficiency. |
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| Published | 2016-11-12 |
| URL | http://arxiv.org/abs/1611.03993v2 |
| http://arxiv.org/pdf/1611.03993v2.pdf | |
| PWC | https://paperswithcode.com/paper/riemannian-tensor-completion-with-side |
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Personalization Effect on Emotion Recognition from Physiological Data: An Investigation of Performance on Different Setups and Classifiers
| Title | Personalization Effect on Emotion Recognition from Physiological Data: An Investigation of Performance on Different Setups and Classifiers |
| Authors | Varvara Kollia |
| Abstract | This paper addresses the problem of emotion recognition from physiological signals. Features are extracted and ranked based on their effect on classification accuracy. Different classifiers are compared. The inter-subject variability and the personalization effect are thoroughly investigated, through trial-based and subject-based cross-validation. Finally, a personalized model is introduced, that would allow for enhanced emotional state prediction, based on the physiological data of subjects that exhibit a certain degree of similarity, without the requirement of further feedback. |
| Tasks | Emotion Recognition |
| Published | 2016-07-20 |
| URL | http://arxiv.org/abs/1607.05832v1 |
| http://arxiv.org/pdf/1607.05832v1.pdf | |
| PWC | https://paperswithcode.com/paper/personalization-effect-on-emotion-recognition |
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Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation
| Title | Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation |
| Authors | Dong Huang, Chang-Dong Wang, Jian-Huang Lai, Yun Liang, Shan Bian, Yu Chen |
| Abstract | Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that restricts the application of SVC lies in its sensitivity to the kernel parameter and the trade-off parameter. Although many extensions of SVC have been developed, to the best of our knowledge, there is still no algorithm that is able to effectively estimate the two crucial parameters in SVC without supervision. In this paper, we propose a novel support vector clustering approach termed ensemble-driven support vector clustering (EDSVC), which for the first time tackles the automatic parameter estimation problem for SVC based on ensemble learning, and is capable of producing robust clustering results in a purely unsupervised manner. Experimental results on multiple real-world datasets demonstrate the effectiveness of our approach. |
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| Published | 2016-08-03 |
| URL | http://arxiv.org/abs/1608.01198v2 |
| http://arxiv.org/pdf/1608.01198v2.pdf | |
| PWC | https://paperswithcode.com/paper/ensemble-driven-support-vector-clustering |
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From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria
| Title | From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria |
| Authors | Asish Ghoshal, Jean Honorio |
| Abstract | In this paper we study the problem of exact recovery of the pure-strategy Nash equilibria (PSNE) set of a graphical game from noisy observations of joint actions of the players alone. We consider sparse linear influence games — a parametric class of graphical games with linear payoffs, and represented by directed graphs of n nodes (players) and in-degree of at most k. We present an $\ell_1$-regularized logistic regression based algorithm for recovering the PSNE set exactly, that is both computationally efficient — i.e. runs in polynomial time — and statistically efficient — i.e. has logarithmic sample complexity. Specifically, we show that the sufficient number of samples required for exact PSNE recovery scales as $\mathcal{O}(\mathrm{poly}(k) \log n)$. We also validate our theoretical results using synthetic experiments. |
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| Published | 2016-07-11 |
| URL | http://arxiv.org/abs/1607.02959v2 |
| http://arxiv.org/pdf/1607.02959v2.pdf | |
| PWC | https://paperswithcode.com/paper/from-behavior-to-sparse-graphical-games |
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Automatic Moth Detection from Trap Images for Pest Management
| Title | Automatic Moth Detection from Trap Images for Pest Management |
| Authors | Weiguang Ding, Graham Taylor |
| Abstract | Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps. Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively. Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to adapt to other species and environments with minimal human effort. It is amenable to implementation on parallel hardware and therefore capable of deployment in settings where real-time performance is required. |
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| Published | 2016-02-24 |
| URL | http://arxiv.org/abs/1602.07383v1 |
| http://arxiv.org/pdf/1602.07383v1.pdf | |
| PWC | https://paperswithcode.com/paper/automatic-moth-detection-from-trap-images-for |
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Near-Optimal Stochastic Approximation for Online Principal Component Estimation
| Title | Near-Optimal Stochastic Approximation for Online Principal Component Estimation |
| Authors | Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang |
| Abstract | Principal component analysis (PCA) has been a prominent tool for high-dimensional data analysis. Online algorithms that estimate the principal component by processing streaming data are of tremendous practical and theoretical interests. Despite its rich applications, theoretical convergence analysis remains largely open. In this paper, we cast online PCA into a stochastic nonconvex optimization problem, and we analyze the online PCA algorithm as a stochastic approximation iteration. The stochastic approximation iteration processes data points incrementally and maintains a running estimate of the principal component. We prove for the first time a nearly optimal finite-sample error bound for the online PCA algorithm. Under the subgaussian assumption, we show that the finite-sample error bound closely matches the minimax information lower bound. |
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| Published | 2016-03-16 |
| URL | http://arxiv.org/abs/1603.05305v4 |
| http://arxiv.org/pdf/1603.05305v4.pdf | |
| PWC | https://paperswithcode.com/paper/near-optimal-stochastic-approximation-for |
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Movement Coordination in Human-Robot Teams: A Dynamical Systems Approach
| Title | Movement Coordination in Human-Robot Teams: A Dynamical Systems Approach |
| Authors | Tariq Iqbal, Samantha Rack, Laurel D. Riek |
| Abstract | In order to be effective teammates, robots need to be able to understand high-level human behavior to recognize, anticipate, and adapt to human motion. We have designed a new approach to enable robots to perceive human group motion in real-time, anticipate future actions, and synthesize their own motion accordingly. We explore this within the context of joint action, where humans and robots move together synchronously. In this paper, we present an anticipation method which takes high-level group behavior into account. We validate the method within a human-robot interaction scenario, where an autonomous mobile robot observes a team of human dancers, and then successfully and contingently coordinates its movements to “join the dance”. We compared the results of our anticipation method to move the robot with another method which did not rely on high-level group behavior, and found our method performed better both in terms of more closely synchronizing the robot’s motion to the team, and also exhibiting more contingent and fluent motion. These findings suggest that the robot performs better when it has an understanding of high-level group behavior than when it does not. This work will help enable others in the robotics community to build more fluent and adaptable robots in the future. |
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| Published | 2016-05-04 |
| URL | http://arxiv.org/abs/1605.01459v1 |
| http://arxiv.org/pdf/1605.01459v1.pdf | |
| PWC | https://paperswithcode.com/paper/movement-coordination-in-human-robot-teams-a |
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On Explore-Then-Commit Strategies
| Title | On Explore-Then-Commit Strategies |
| Authors | Aurélien Garivier, Emilie Kaufmann, Tor Lattimore |
| Abstract | We study the problem of minimising regret in two-armed bandit problems with Gaussian rewards. Our objective is to use this simple setting to illustrate that strategies based on an exploration phase (up to a stopping time) followed by exploitation are necessarily suboptimal. The results hold regardless of whether or not the difference in means between the two arms is known. Besides the main message, we also refine existing deviation inequalities, which allow us to design fully sequential strategies with finite-time regret guarantees that are (a) asymptotically optimal as the horizon grows and (b) order-optimal in the minimax sense. Furthermore we provide empirical evidence that the theory also holds in practice and discuss extensions to non-gaussian and multiple-armed case. |
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| Published | 2016-05-29 |
| URL | http://arxiv.org/abs/1605.08988v2 |
| http://arxiv.org/pdf/1605.08988v2.pdf | |
| PWC | https://paperswithcode.com/paper/on-explore-then-commit-strategies |
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A Streaming Algorithm for Crowdsourced Data Classification
| Title | A Streaming Algorithm for Crowdsourced Data Classification |
| Authors | Thomas Bonald, Richard Combes |
| Abstract | We propose a streaming algorithm for the binary classification of data based on crowdsourcing. The algorithm learns the competence of each labeller by comparing her labels to those of other labellers on the same tasks and uses this information to minimize the prediction error rate on each task. We provide performance guarantees of our algorithm for a fixed population of independent labellers. In particular, we show that our algorithm is optimal in the sense that the cumulative regret compared to the optimal decision with known labeller error probabilities is finite, independently of the number of tasks to label. The complexity of the algorithm is linear in the number of labellers and the number of tasks, up to some logarithmic factors. Numerical experiments illustrate the performance of our algorithm compared to existing algorithms, including simple majority voting and expectation-maximization algorithms, on both synthetic and real datasets. |
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| Published | 2016-02-23 |
| URL | http://arxiv.org/abs/1602.07107v1 |
| http://arxiv.org/pdf/1602.07107v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-streaming-algorithm-for-crowdsourced-data |
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Thompson Sampling is Asymptotically Optimal in General Environments
| Title | Thompson Sampling is Asymptotically Optimal in General Environments |
| Authors | Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter |
| Abstract | We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its value converges to the optimal value in mean and (2) given a recoverability assumption regret is sublinear. |
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| Published | 2016-02-25 |
| URL | http://arxiv.org/abs/1602.07905v2 |
| http://arxiv.org/pdf/1602.07905v2.pdf | |
| PWC | https://paperswithcode.com/paper/thompson-sampling-is-asymptotically-optimal |
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Semi-Supervised Learning on Graphs through Reach and Distance Diffusion
| Title | Semi-Supervised Learning on Graphs through Reach and Distance Diffusion |
| Authors | Edith Cohen |
| Abstract | Semi-supervised learning (SSL) is an indispensable tool when there are few labeled entities and many unlabeled entities for which we want to predict labels. With graph-based methods, entities correspond to nodes in a graph and edges represent strong relations. At the heart of SSL algorithms is the specification of a dense {\em kernel} of pairwise affinity values from the graph structure. A learning algorithm is then trained on the kernel together with labeled entities. The most popular kernels are {\em spectral} and include the highly scalable “symmetric” Laplacian methods, that compute a soft labels using Jacobi iterations, and “asymmetric” methods including Personalized Page Rank (PPR) which use short random walks and apply with directed relations, such as like, follow, or hyperlinks. We introduce {\em Reach diffusion} and {\em Distance diffusion} kernels that build on powerful social and economic models of centrality and influence in networks and capture the directed pairwise relations that underline social influence. Inspired by the success of social influence as an alternative to spectral centrality such as Page Rank, we explore SSL with our kernels and develop highly scalable algorithms for parameter setting, label learning, and sampling. We perform preliminary experiments that demonstrate the properties and potential of our kernels. |
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| Published | 2016-03-30 |
| URL | http://arxiv.org/abs/1603.09064v5 |
| http://arxiv.org/pdf/1603.09064v5.pdf | |
| PWC | https://paperswithcode.com/paper/semi-supervised-learning-on-graphs-through |
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Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training
| Title | Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training |
| Authors | Anirban Santara, Debapriya Maji, DP Tejas, Pabitra Mitra, Arobinda Gupta |
| Abstract | Deep neural networks are capable of modelling highly non-linear functions by capturing different levels of abstraction of data hierarchically. While training deep networks, first the system is initialized near a good optimum by greedy layer-wise unsupervised pre-training. However, with burgeoning data and increasing dimensions of the architecture, the time complexity of this approach becomes enormous. Also, greedy pre-training of the layers often turns detrimental by over-training a layer causing it to lose harmony with the rest of the network. In this paper a synchronized parallel algorithm for pre-training deep networks on multi-core machines has been proposed. Different layers are trained by parallel threads running on different cores with regular synchronization. Thus the pre-training process becomes faster and chances of over-training are reduced. This is experimentally validated using a stacked autoencoder for dimensionality reduction of MNIST handwritten digit database. The proposed algorithm achieved 26% speed-up compared to greedy layer-wise pre-training for achieving the same reconstruction accuracy substantiating its potential as an alternative. |
| Tasks | Dimensionality Reduction |
| Published | 2016-03-09 |
| URL | http://arxiv.org/abs/1603.02836v1 |
| http://arxiv.org/pdf/1603.02836v1.pdf | |
| PWC | https://paperswithcode.com/paper/faster-learning-of-deep-stacked-autoencoders |
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Training a Feedback Loop for Hand Pose Estimation
| Title | Training a Feedback Loop for Hand Pose Estimation |
| Authors | Markus Oberweger, Paul Wohlhart, Vincent Lepetit |
| Abstract | We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop. The components of this feedback loop are also Deep Networks, optimized using training data. They remove the need for fitting a 3D model to the input data, which requires both a carefully designed fitting function and algorithm. We show that our approach outperforms state-of-the-art methods, and is efficient as our implementation runs at over 400 fps on a single GPU. |
| Tasks | Hand Pose Estimation, Pose Estimation |
| Published | 2016-09-30 |
| URL | http://arxiv.org/abs/1609.09698v1 |
| http://arxiv.org/pdf/1609.09698v1.pdf | |
| PWC | https://paperswithcode.com/paper/training-a-feedback-loop-for-hand-pose |
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