Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference

• URL: http://arxiv.org/abs/2105.11492v1
• Date: Mon, 24 May 2021 18:34:46 GMT
• Title: Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference
• Authors: Mohamadreza Sheibani, Ge Ou
• Abstract summary: Post-earthquake regional damage assessment of buildings is an expensive task. The information theoretic measure of mutual information is one of the most effective criteria to evaluate the effectiveness of the samples. A local kernels strategy was proposed to reduce the computational costs, but the adaptability of the kernels to the observed labels was not considered. In this article, an adaptive local kernels methodology is developed that allows for the conformability of the kernels to the observed output data.
• Score: 1.066048003460524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The abundance of training data is not guaranteed in various supervised learning applications. One of these situations is the post-earthquake regional damage assessment of buildings. Querying the damage label of each building requires a thorough inspection by experts, and thus, is an expensive task. A practical approach is to sample the most informative buildings in a sequential learning scheme. Active learning methods recommend the most informative cases that are able to maximally reduce the generalization error. The information theoretic measure of mutual information (MI) is one of the most effective criteria to evaluate the effectiveness of the samples in a pool-based sample selection scenario. However, the computational complexity of the standard MI algorithm prevents the utilization of this method on large datasets. A local kernels strategy was proposed to reduce the computational costs, but the adaptability of the kernels to the observed labels was not considered in the original formulation of this strategy. In this article, an adaptive local kernels methodology is developed that allows for the conformability of the kernels to the observed output data while enhancing the computational complexity of the standard MI algorithm. The proposed algorithm is developed to work on a Gaussian process regression (GPR) framework, where the kernel hyperparameters are updated after each label query using the maximum likelihood estimation. In the sequential learning procedure, the updated hyperparameters can be used in the MI kernel matrices to improve the sample suggestion performance. The advantages are demonstrated on a simulation of the 2018 Anchorage, AK, earthquake. It is shown that while the proposed algorithm enables GPR to reach acceptable performance with fewer training data, the computational demands remain lower than the standard local kernels strategy.

Related papers

• MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering [3.9146761527401424]
  This research proposes a novel approach called the Modified Isolation Kernel (MIK) as an alternative to the Gaussian kernel. MIK uses adaptive density estimation to capture local structures more accurately and integrates robustness measures. It exhibits improved preservation of the local and global structure and enables better visualization of clusters and subclusters in the embedded space.
  arXiv  Detail & Related papers  (2024-10-21T06:57:09Z)
• Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach [51.76826149868971]
  Policy evaluation via Monte Carlo simulation is at the core of many MC Reinforcement Learning (RL) algorithms. We propose as a quality index a surrogate of the mean squared error of a return estimator that uses trajectories of different lengths. We present an adaptive algorithm called Robust and Iterative Data collection strategy Optimization (RIDO)
  arXiv  Detail & Related papers  (2024-10-17T11:47:56Z)
• Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning [88.78080749909665]
  Current on-device training methods just focus on efficient training without considering the catastrophic forgetting. This paper proposes a simple but effective edge-friendly incremental learning framework. Our method achieves average accuracy boost of 38.08% with even less memory and approximate computation.
  arXiv  Detail & Related papers  (2024-06-13T05:49:29Z)
• Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling [5.584863079768593]
  We introduce a flexible two-stage GPR framework that separates mean prediction and uncertainty quantification (UQ) to prevent mean misspecification. We also propose a kernel function misspecification algorithm, supported by theoretical analysis, that selects the optimal kernel from a candidate set. With much lower computational cost, our subsampling-based strategy can yield competitive or better performance than training exclusively on the full dataset.
  arXiv  Detail & Related papers  (2024-05-22T16:11:29Z)
• Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels [57.46832672991433]
  We propose a novel equation discovery method based on Kernel learning and BAyesian Spike-and-Slab priors (KBASS) We use kernel regression to estimate the target function, which is flexible, expressive, and more robust to data sparsity and noises. We develop an expectation-propagation expectation-maximization algorithm for efficient posterior inference and function estimation.
  arXiv  Detail & Related papers  (2023-10-09T03:55:09Z)
• Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics [0.4588028371034406]
  We introduce algorithms to select/design kernels in Gaussian process regression/kriging surrogate modeling techniques. A first class of algorithms is kernel flow, which was introduced in a context of classification in machine learning. A second class of algorithms is called spectral kernel ridge regression, and aims at selecting a "best" kernel such that the norm of the function to be approximated is minimal.
  arXiv  Detail & Related papers  (2022-06-03T07:50:54Z)
• Federated Minimax Optimization: Improved Convergence Analyses and Algorithms [32.062312674333775]
  We consider non minimax optimization, is gaining prominence many modern machine learning applications such as GANs. We provide a novel and tighter analysis algorithm, improves convergence communication guarantees in the existing literature.
  arXiv  Detail & Related papers  (2022-03-09T16:21:31Z)
• Large-scale Optimization of Partial AUC in a Range of False Positive Rates [51.12047280149546]
  The area under the ROC curve (AUC) is one of the most widely used performance measures for classification models in machine learning. We develop an efficient approximated gradient descent method based on recent practical envelope smoothing technique. Our proposed algorithm can also be used to minimize the sum of some ranked range loss, which also lacks efficient solvers.
  arXiv  Detail & Related papers  (2022-03-03T03:46:18Z)
• Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints [15.296955630621566]
  Constr-DRKM is a deep kernel method for the unsupervised learning of disentangled data representations. We quantitatively evaluate the proposed method's effectiveness in disentangled feature learning.
  arXiv  Detail & Related papers  (2020-11-25T11:40:10Z)
• Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing [113.52575069030192]
  Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
  arXiv  Detail & Related papers  (2020-10-02T10:41:59Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.