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Fast and robust supervised machine learning approach for classification and prediction or Parkinson's disease onset

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2021年第6期9卷 690-706页

作者： Bollipo, Lavanya Madhuri Kadambari, K. V. Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal Andhra Pradesh India

Parkinson's disease (PD) is an incurable long-term neurodegenerative disorder that mainly influence the motor system and eventually results in significant morbidity. The use of computational tools for classification and prediction of PD is desirable for detecting the symptoms at the onset. In this paper, we developed a novel machine learning technique based on incremental support vector machine and modified frank-wolfe method (SVM-MFW) for classification and prediction of PD onset. The proposed model works on heterogeneous data gathered from the Parkinson's progression markers initiative (PPMI) data repository. The work when compared to earlier state-of-the-art techniques outplays them in terms of both classification and prediction. It is shown that the model achieves an equivalent accuracy of 98.3% and a low cross-entropy value in less computational time.

关键词： Parkinson's disease support vector machine incremental learning frank-wolfe algorithm prediction and classification

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Statistical Guarantees and algorithmic Convergence Issues of Variational Boosting 33

Statistical Guarantees and Algorithmic Convergence Issues of...

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IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Guha, Biraj Subhra Bhattacharya, Anirban Pati, Debdeep Univ Rochester Med Ctr Dept Biostat & Computat Biol Rochester NY 14642 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA

ISBN: (纸本)9781665408981

We provide statistical guarantees for Bayesian variational boosting by proposing a novel small bandwidth Gaussian mixture variational family. We employ a functional version of frank-wolfe optimization as our variational algorithm and study frequentist properties of the iterative boosting updates. Comparisons are drawn to the recent literature on boosting, describing how the choice of the variational family and the discrepancy measure affect both convergence and finite-sample statistical properties of the optimization routine. Specifically, we first demonstrate stochastic boundedness of the boosting iterates with respect to the data generating distribution. We next integrate this within our algorithm to provide an explicit convergence rate, ending with a result on the required number of boosting updates.

关键词： Approximate Bayesian Inference Variational Inference Boosting Kullback-Leibler Divergence frank-wolfe algorithm Convergence Rate Gaussian Mixtures

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On the rotational invariant L1-norm PCA

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LINEAR ALGEBRA AND ITS APPLICATIONS 2020年 587卷 243-270页

作者： Neumayer, Sebastian Nimmer, Max Setzer, Simon Steidl, Gabriele Tech Univ Kaiserslautern Paul Ehrlich Str 31 D-67663 Kaiserslautern Germany Engineers Gate London England Fraunhofer ITWM Fraunhofer Pl 1 D-67663 Kaiserslautern Germany

Principal component analysis (PCA) is a powerful tool for dimensionality reduction. Unfortunately, it is sensitive to outliers, so that various robust PCA variants were proposed in the literature. One of the most frequently applied methods for high dimensional data reduction is the rotational invariant L-1-norm PCA of Ding and coworkers. So far no convergence proof for this algorithm was available. The main topic of this paper is to fill this gap. We reinterpret this robust approach as a conditional gradient algorithm and show moreover that it coincides with a gradient descent algorithm on Grassmann manifolds. Based on the latter point of view, we prove global convergence of the whole series of iterates to a critical point using the Kurdyka-Lojasiewicz property of the objective function, where we have to pay special attention to so-called anchor points, where the function is not differentiable. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Principal component analysis Dimensionality reduction Robust subspace fitting Conditional gradient algorithm frank-wolfe algorithm Optimization on Grassmann manifolds

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Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

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JOURNAL OF MACHINE LEARNING RESEARCH 2020年第1期21卷 1-49页

作者： Mokhtari, Aryan Hassani, Hamed Karbasi, Amin Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Yale Univ Dept Elect Engn & Comp Sci New Haven CT 06520 USA

This paper considers stochastic optimization problems for a large class of objective functions, including convex and continuous submodular. Stochastic proximal gradient methods have been widely used to solve such problems;however, their applicability remains limited when the problem dimension is large and the projection onto a convex set is computationally costly. Instead, stochastic conditional gradient algorithms are proposed as an alternative solution which rely on (i) Approximating gradients via a simple averaging technique requiring a single stochastic gradient evaluation per iteration;(ii) Solving a linear program to compute the descent/ascent direction. The gradient averaging technique reduces the noise of gradient approximations as time progresses, and replacing projection step in proximal methods by a linear program lowers the computational complexity of each iteration. We show that under convexity and smoothness assumptions, our proposed stochastic conditional gradient method converges to the optimal objective function value at a sublinear rate of O(1/t(1/3)). Further, for a monotone and continuous DR-submodular function and subject to a general convex body constraint, we prove that our proposed method achieves a ((1-1/e)OPT-epsilon) guarantee (in expectation) with O(1/epsilon(3)) stochastic gradient computations. This guarantee matches the known hardness results and closes the gap between deterministic and stochastic continuous submodular maximization. Additionally, we achieve ((1/e)OPT-epsilon) guarantee after operating on O(1/epsilon(3)) stochastic gradients for the case that the objective function is continuous DR-submodular but non-monotone and the constraint set is a down-closed convex body. By using stochastic continuous optimization as an interface, we also provide the first (1-1/e) tight approximation guarantee for maximizing a monotone but stochastic submodular set function subject to a general matroid constraint and (1/e) approximation guarantee

关键词： Stochastic optimization conditional gradient methods convex minimization submodular maximization gradient averaging frank-wolfe algorithm greedy algorithm

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Safe Squeezing for Antisparse Coding

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 3252-3265页

作者： Elvira, Clement Herzet, Cedric Univ Rennes INRIA CNRS IRISA F-35000 Rennes France

Spreading the information over all coefficients of a representation is a desirable property in many applications such as digital communication or machine learning. This so-called antisparse representation can be obtained by solving a convex program involving an $\ell _\infty$-norm penalty combined with a quadratic discrepancy. In this paper, we propose a new methodology, dubbed safe squeezing, to accelerate the computation of antisparse representation. We describe a test that allows to detect saturated entries in the solution of the optimization problem. The contribution of these entries is compacted into a single vector, thus operating a form of dimensionality reduction. We propose two algorithms to solve the resulting lower dimensional problem. Numerical experiments show the effectiveness of the proposed method to detect the saturated components of the solution and illustrates the induced computational gains in the resolution of the antisparse problem.

关键词： Dimensionality reduction Signal processing algorithms Optimization Convex functions Encoding Antisparse coding safe screening scaled projected-gradient algorithm frank-wolfe algorithm

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Robust linear model for multi-period AC optimal power flow

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第13期14卷 2535-2548页

作者： Heidarabadi, Houman Pourahmadi, Farzaneh Hossieni, Seyed Hamid Sharif Univ Technol Dept Elect Engn Ctr Excellence Power Syst Control & Management Tehran Iran

In the near future power systems, efficient management of uncertainties with considering the system constraints without any simplification will be a challenge for system operators. Considering AC constraints leads to providing more accurate schedule of generating units, which can have a significant impact on the reduction of operating costs. Although numerous studies have been done to convexify AC optimal power flow constraints, most of the models are non-linear, which can be intractable for large-scale systems. In this study, a novel linear robust AC model is introduced using a combination of the quadratic convex relaxation (QCR) and the frank-wolfe algorithm for linearising the AC constraints. The uncertainties are modelled by applying the robust optimisation with recourse to obtain an optimal schedule for the conventional units in multi-period real-time markets. The Benders-dual algorithm is implemented to solve the optimisation problem. The proposed model was applied to the IEEE 3-bus, 118-bus, and 300-bus systems. The results indicate that the proposed algorithm obtains more precise approximation than the QCR method. In addition, the costs and losses of the proposed model are less than those of the conventional robust DC and stochastic models. Furthermore, because the proposed model is linear, its runtime is rational.

关键词： convex programming optimisation load flow quadratic programming stochastic processes robust optimisation optimal schedule conventional units Benders-dual algorithm 300-bus systems conventional robust DC stochastic models robust linear model multiperiod AC optimal power flow future power systems system constraints independent system operators accurate schedule generating units operating costs AC optimal power flow constraints large-scale systems novel linear robust AC model quadratic convex relaxation frank-wolfe algorithm nonlinear AC constraints

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Dynamic Scheduling for IoT Analytics at the Edge

Dynamic Scheduling for IoT Analytics at the Edge

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21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM)

作者： Galanopoulos, Apostolos Valls, Victor Leith, Douglas J. Iosifidis, George Trinity Coll Dublin Sch Comp Sci & Stat Dublin Ireland Yale Univ Dept Elect Engn New Haven CT 06520 USA Yale Univ Inst Network Sci New Haven CT 06520 USA

ISBN: (纸本)9781728173740

We propose an online policy that schedules the transmission and processing of data analytic tasks in an Internet of Things (IoT) network. The tasks are executed with different precision at the (possibly heterogeneous) nodes;the network is subject to link bandwidth and node processing capacity changes;and the task requests vary following unknown statistics. For this general IoT scenario, we formulate a resource allocation problem towards maximizing the aggregate tasks precision, and design a dynamic solution policy by combining the frankwolfe and dual subgradient algorithms. Our policy (FWDS) is guaranteed to converge within bounded distance from the optimal solution, while ensuring interference-free transmissions, and being oblivious to network and/or traffic load changes. We use a wireless testbed and a state-of-the-art face recognition application to implement FWDS and compare it with static or dynamic (maxweight-type) competitor policies. Our findings verify that FWDS pushes the envelope of network control algorithms by handling time-varying objectives and possibly non-i.i.d. network/load statistics, with smaller complexity than its competitors.

关键词： frank-wolfe algorithm Internet of Things Data Analytics Network Optimization

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Projection-free decentralized online learning for submodular maximization over time-varying networks

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The Journal of Machine Learning Research 2021年第1期22卷 2328-2369页

作者： Junlong Zhu Qingtao Wu Mingchuan Zhang Ruijuan Zheng Keqin Li School of Information Engineering Henan University of Science and Technology Luoyang China Department of Computer Science State University of New York New Paltz NY

This paper considers a decentralized online submodular maximization problem over time-varying networks, where each agent only utilizes its own information and the received information from its neighbors. To address the problem, we propose a decentralized Meta-frank-wolfe online learning method in the adversarial online setting by using local communication and local computation. Moreover, we show that an expected regret bound of O(√T) is achieved with (1 &mdash 1/e) approximation guarantee, where T is a time horizon. In addition, we also propose a decentralized one-shot frank-wolfe online learning method in the stochastic online setting. Furthermore, we also show that an expected regret bound O(T2/3) is obtained with (1 &mdash 1/e) approximation guarantee. Finally, we confirm the theoretical results via various experiments on different datasets.

关键词： expected regret bound frank-wolfe algorithm submodular maximization

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PROJECTION FREE DYNAMIC ONLINE LEARNING

PROJECTION FREE DYNAMIC ONLINE LEARNING

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kalhan, Deepak S. Bedi, Amrit S. Koppel, Alec Rajawat, Ketan Gupta, Abhishek Banerjee, Adrish IIT Kanpur Dept Elect Engn Kanpur Uttar Pradesh India US Army Res Lab CISD Adelphi MD USA

ISBN: (纸本)9781509066315

Projection based algorithms are popular in the literature for online convex optimization with convex constraints and the projection step results in a bottleneck for the practical implementation of the algorithms. To avoid this bottleneck, we propose a projection-free scheme based on frank-wolfe: where instead of online gradient steps, we use steps that are collinear with the gradient but guaranteed to be feasible. We establish performance in terms of dynamic regret, which quantifies cost accumulation as compared with the optimal at each individual time slot. Specifically, for convex losses, we establish O(T-1/2) dynamic regret up to metrics of non-stationarity. We relax the algorithm's required information to only noisy gradient estimates, i.e., partial feedback and derived the dynamic regret bounds. Experiments on matrix completion problem and background separation in video demonstrate favorable performance of the proposed scheme.

关键词： Online learning frank-wolfe algorithm convex optimization gradient descent

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A distributed frank-wolfe framework for learning low-rank matrices with the trace norm

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MACHINE LEARNING 2018年第8-10期107卷 1457-1475页

作者： Zheng, Wenjie Bellet, Aarelien Gallinari, Patrick UPMC Univ Paris 06 Sorbonne Univ UMR 7606 LIP6 Paris France INRIA Paris France

We consider the problem of learning a high-dimensional but low-rank matrix from a large-scale dataset distributed over several machines, where low-rankness is enforced by a convex trace norm constraint. We propose DFW- TRACE, a distributed frank-wolfe algorithm which leverages the low-rank structure of its updates to achieve efficiency in time, memory and communication usage. The step at the heart of DFW- TRACE is solved approximately using a distributed version of the power method. We provide a theoretical analysis of the convergence of DFW- TRACE, showing that we can ensure sublinear convergence in expectation to an optimal solution with few power iterations per epoch. We implement DFW- TRACE in the Apache Spark distributed programming framework and validate the usefulness of our approach on synthetic and real data, including the ImageNetdataset with high-dimensional features extracted from a deep neural network.

关键词： frank-wolfe algorithm Low-rank learning Trace norm Distributed optimization Multi-task learning Multinomial logistic regression

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