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Learning methods

Xiaoming Zhang, Senzhang Wang, Zhoujun Li, Shuai Ma
Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content...
June 20, 2017: IEEE Transactions on Cybernetics
Yue-Jiao Gong, Jun Zhang, Yicong Zhou
Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning...
June 20, 2017: IEEE Transactions on Neural Networks and Learning Systems
Jiwen Lu, Junlin Hu, Yap-Peng Tan
This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in face and kinship verification, most existing metric learning methods aim to learn a single Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, which cannot capture the nonlinear manifold where face images usually lie on. To address this, we propose a DDML method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged, respectively...
June 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
M Amac Guvensan, A Oguz Kansiz, N Cihan Camgoz, H Irem Turkmen, A Gokhan Yavuz, M Elif Karsligil
Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms...
June 23, 2017: Sensors
Nguyen-Quoc-Khanh Le, Quang-Thai Ho, Yu-Yen Ou
In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80...
June 22, 2017: Journal of Computational Chemistry
M Manczinger, V Bodnár, B T Papp, B Sz Bolla, K Szabó, B Balázs, E Csányi, E Szél, G Erős, L Kemény
As drug development is extremely expensive, the identification of novel indications for in-market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are currently not available. We developed an algorithm that simulates drug effects on the flow of information through protein-protein interaction networks, and uses Support Vector Machine to identify potentially effective drugs in our model disease, psoriasis...
June 23, 2017: Clinical Pharmacology and Therapeutics
Yuan Luo, William K Thompson, Timothy M Herr, Zexian Zeng, Mark A Berendsen, Siddhartha R Jonnalagadda, Matthew B Carson, Justin Starren
The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions...
June 22, 2017: Drug Safety: An International Journal of Medical Toxicology and Drug Experience
Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen
Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Eva Friedel, Miriam Sebold, Sören Kuitunen-Paul, Stephan Nebe, Ilya M Veer, Ulrich S Zimmermann, Florian Schlagenhauf, Michael N Smolka, Michael Rapp, Henrik Walter, Andreas Heinz
Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding...
2017: Frontiers in Human Neuroscience
Juanjuan Zhang, Pieter Fiers, Kirby A Witte, Rachel W Jackson, Katherine L Poggensee, Christopher G Atkeson, Steven H Collins
Exoskeletons and active prostheses promise to enhance human mobility, but few have succeeded. Optimizing device characteristics on the basis of measured human performance could lead to improved designs. We have developed a method for identifying the exoskeleton assistance that minimizes human energy cost during walking. Optimized torque patterns from an exoskeleton worn on one ankle reduced metabolic energy consumption by 24.2 ± 7.4% compared to no torque. The approach was effective with exoskeletons worn on one or both ankles, during a variety of walking conditions, during running, and when optimizing muscle activity...
June 23, 2017: Science
Gabriela Sehnem Heck, Val Oliveira Pintro, Richard Rene Pereira, Mauricio Boff de Ávila, Nayara Maria Bernhardt Levin, Walter Filgueira de Azevedo
BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. OBJECTIVE: Our goal here is to review recent developments in the application of machine learning methods to predict ligand- binding affinity...
June 22, 2017: Current Medicinal Chemistry
Yu-Yan Hu, Li Li, Xiao-Hui Xian, Min Zhang, Xiao-Cai Sun, Shu-Qin Li, Xin Cui, Jie Qi, Wen-Bin Li
Glutamate is the primary excitatory neurotransmitter in the mammalian central nervous system, which plays an important role in many aspects of normal brain function such as neural development, motor functions, learning and memory etc. However, excessive accumulation of glutamate in the extracellular fluid will induce excitotoxicity which is considered to be a major mechanism of cell death in brain ischemia. There is no enzyme to decompose the glutamate in extracellular fluid, so extracellular glutamate homeostasis within the central nervous system is mainly regulated by the uptake activity of excitatory amino acid transporters...
June 22, 2017: Current Pharmaceutical Design
Shintami C Hidayati, Chuang-Wen You, Wen-Huang Cheng, Kai-Lung Hua
According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory...
June 19, 2017: IEEE Transactions on Cybernetics
Zhigang Ma, Xiaojun Chang, Zhongwen Xu, Nicu Sebe, Alexander G Hauptmann
Semantic attributes have been increasingly used the past few years for multimedia event detection (MED) with promising results. The motivation is that multimedia events generally consist of lower level components such as objects, scenes, and actions. By characterizing multimedia event videos with semantic attributes, one could exploit more informative cues for improved detection results. Much existing work obtains semantic attributes from images, which may be suboptimal for video analysis since these image-inferred attributes do not carry dynamic information that is essential for videos...
June 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
Hong Tao, Chenping Hou, Feiping Nie, Jubo Zhu, Dongyun Yi
With the advent of multi-view data, multi-view learning has become an important research direction in machine learning and image processing. Considering the difficulty of obtaining labeled data in many machine learning applications, we focus on the multi-view semi-supervised classification problem. In this paper, we propose an algorithm named Multi-View Semi-Supervised Classification via Adaptive Regression (MVAR) to address this problem. Specifically, regression based loss functions with ℓ2,1 matrix norm are adopted for each view and the final objective function is formulated as the linear weighted combination of all the loss functions...
June 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Mengqiu Hu, Yang Yang, Fumin Shen, Luming Zhang, Heng Tao Shen, Xuelong Li
Driven by the rapid development of Internet and digital technologies, we have witnessed the explosive growth of Web images in recent years. Seeing that labels can reflect the semantic contents of the images, automatic image annotation, which can further facilitate the procedure of image semantic indexing, retrieval and other image management tasks, has become one of the most crucial research directions in multimedia. Most of the existing annotation methods heavily rely on well-labeled training data (expensive to collect) and/or single view of visual features (insufficient representative power)...
June 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Reuben A Farrugia, Christine Guillemot
Most face super-resolution methods assume that low- and high-resolution manifolds have similar local geometrical structure, hence learn local models on the low-resolution manifold (e.g. sparse or locally linear embedding models), which are then applied on the high- resolution manifold. However, the low-resolution manifold is distorted by the one-to-many relationship between low- and high- resolution patches. This paper presents the Linear Model of Coupled Sparse Support (LM-CSS) method which learns linear models based on the local geometrical structure on the high-resolution manifold rather than on the low-resolution manifold...
June 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, Michael Unser
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution...
June 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps...
June 15, 2017: IEEE Transactions on Medical Imaging
Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Liu
Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications...
June 15, 2017: IEEE Transactions on Bio-medical Engineering
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