487 research outputs found
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Background subtraction has been a fundamental and widely studied task in
video analysis, with a wide range of applications in video surveillance,
teleconferencing and 3D modeling. Recently, motivated by compressive imaging,
background subtraction from compressive measurements (BSCM) is becoming an
active research task in video surveillance. In this paper, we propose a novel
tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames
into backgrounds with spatial-temporal correlations and foregrounds with
spatio-temporal continuity in a tensor framework. In this approach, we use 3D
total variation (TV) to enhance the spatio-temporal continuity of foregrounds,
and Tucker decomposition to model the spatio-temporal correlations of video
background. Based on this idea, we design a basic tensor RPCA model over the
video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize
the correlations among the groups of similar 3D patches of video background, we
further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint
tensor Tucker decompositions of 3D patch groups for modeling the video
background. Efficient algorithms using alternating direction method of
multipliers (ADMM) are developed to solve the proposed models. Extensive
experiments on simulated and real-world videos demonstrate the superiority of
the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI
Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness
In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt
Non-negative Matrix Factorization: A Survey
CAUL read and publish agreement 2022Publishe
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
Across machine learning, the use of curricula has shown strong empirical
potential to improve learning from data by avoiding local optima of training
objectives. For reinforcement learning (RL), curricula are especially
interesting, as the underlying optimization has a strong tendency to get stuck
in local optima due to the exploration-exploitation trade-off. Recently, a
number of approaches for an automatic generation of curricula for RL have been
shown to increase performance while requiring less expert knowledge compared to
manually designed curricula. However, these approaches are seldomly
investigated from a theoretical perspective, preventing a deeper understanding
of their mechanics. In this paper, we present an approach for automated
curriculum generation in RL with a clear theoretical underpinning. More
precisely, we formalize the well-known self-paced learning paradigm as inducing
a distribution over training tasks, which trades off between task complexity
and the objective to match a desired task distribution. Experiments show that
training on this induced distribution helps to avoid poor local optima across
RL algorithms in different tasks with uninformative rewards and challenging
exploration requirements
Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection
Millimeter-wave (MMW) imaging is emerging as a promising technique for safe
security inspection. It achieves a delicate balance between imaging resolution,
penetrability and human safety, resulting in higher resolution compared to
low-frequency microwave, stronger penetrability compared to visible light, and
stronger safety compared to X ray. Despite of recent advance in the last
decades, the high cost of requisite large-scale antenna array hinders
widespread adoption of MMW imaging in practice. To tackle this challenge, we
report a large-scale single-shot MMW imaging framework using sparse antenna
array, achieving low-cost but high-fidelity security inspection under an
interpretable learning scheme. We first collected extensive full-sampled MMW
echoes to study the statistical ranking of each element in the large-scale
array. These elements are then sampled based on the ranking, building the
experimentally optimal sparse sampling strategy that reduces the cost of
antenna array by up to one order of magnitude. Additionally, we derived an
untrained interpretable learning scheme, which realizes robust and accurate
image reconstruction from sparsely sampled echoes. Last, we developed a neural
network for automatic object detection, and experimentally demonstrated
successful detection of concealed centimeter-sized targets using 10% sparse
array, whereas all the other contemporary approaches failed at the same sample
sampling ratio. The performance of the reported technique presents higher than
50% superiority over the existing MMW imaging schemes on various metrics
including precision, recall, and mAP50. With such strong detection ability and
order-of-magnitude cost reduction, we anticipate that this technique provides a
practical way for large-scale single-shot MMW imaging, and could advocate its
further practical applications
A new integrated framework for the identification of potential virus–drug associations
IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.MethodsIn this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.ResultsThe results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes
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