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Journal articles on the topic 'Kernel mean embedding'

Author: Grafiati

Published: 26 October 2024

Last updated: 31 July 2025

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1

Berquin, Yann. "Kernel mean embedding vs kernel density estimation: A quantum perspective." Physics Letters A 528 (December 2024): 130047. http://dx.doi.org/10.1016/j.physleta.2024.130047.

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Jorgensen, Palle E. T., Myung-Sin Song, and James Tian. "Conditional mean embedding and optimal feature selection via positive definite kernels." Opuscula Mathematica 44, no. 1 (2024): 79–103. http://dx.doi.org/10.7494/opmath.2024.44.1.79.

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Motivated by applications, we consider new operator-theoretic approaches to conditional mean embedding (CME). Our present results combine a spectral analysis-based optimization scheme with the use of kernels, stochastic processes, and constructive learning algorithms. For initially given non-linear data, we consider optimization-based feature selections. This entails the use of convex sets of kernels in a construction o foptimal feature selection via regression algorithms from learning models. Thus, with initial inputs of training data (for a suitable learning algorithm), each choice of a kern

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Chen, Wei, Jun-Xiang Mao, and Min-Ling Zhang. "Learnware Specification via Label-Aware Neural Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15857–65. https://doi.org/10.1609/aaai.v39i15.33741.

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The learnware paradigm aims to establish a learnware dock system of numerous well-trained machine learning models, enabling users to reuse existing helpful models for their tasks instead of starting from scratch. Each learnware in the system is a well-established model submitted by its developer, associated with a specification generated by the learnware dock system. The specification characterizes the specialty of the corresponding model, enabling it to be identified accurately for new task requirements. Existing specification generation methods are mostly based on the Reduced Kernel Mean Emb

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Muandet, Krikamol, Kenji Fukumizu, Bharath Sriperumbudur, and Bernhard Schölkopf. "Kernel Mean Embedding of Distributions: A Review and Beyond." Foundations and Trends® in Machine Learning 10, no. 1-2 (2017): 1–141. http://dx.doi.org/10.1561/2200000060.

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Van Hauwermeiren, Daan, Michiel Stock, Thomas De Beer, and Ingmar Nopens. "Predicting Pharmaceutical Particle Size Distributions Using Kernel Mean Embedding." Pharmaceutics 12, no. 3 (2020): 271. http://dx.doi.org/10.3390/pharmaceutics12030271.

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In the pharmaceutical industry, the transition to continuous manufacturing of solid dosage forms is adopted by more and more companies. For these continuous processes, high-quality process models are needed. In pharmaceutical wet granulation, a unit operation in the ConsiGma TM -25 continuous powder-to-tablet system (GEA Pharma systems, Collette, Wommelgem, Belgium), the product under study presents itself as a collection of particles that differ in shape and size. The measurement of this collection results in a particle size distribution. However, the theoretical basis to describe the physica

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Xu, Bi-Cun, Kai Ming Ting, and Yuan Jiang. "Isolation Graph Kernel." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10487–95. http://dx.doi.org/10.1609/aaai.v35i12.17255.

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A recent Wasserstein Weisfeiler-Lehman (WWL) Graph Kernel has a distinctive feature: Representing the distribution of Weisfeiler-Lehman (WL)-embedded node vectors of a graph in a histogram that enables a dissimilarity measurement of two graphs using Wasserstein distance. It has been shown to produce better classification accuracy than other graph kernels which do not employ such distribution and Wasserstein distance. This paper introduces an alternative called Isolation Graph Kernel (IGK) that measures the similarity between two attributed graphs. IGK is unique in two aspects among existing gr

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Rustamov, Raif M., and James T. Klosowski. "Kernel mean embedding based hypothesis tests for comparing spatial point patterns." Spatial Statistics 38 (August 2020): 100459. http://dx.doi.org/10.1016/j.spasta.2020.100459.

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Hou, Boya, Sina Sanjari, Nathan Dahlin, and Subhonmesh Bose. "Compressed Decentralized Learning of Conditional Mean Embedding Operators in Reproducing Kernel Hilbert Spaces." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 7902–9. http://dx.doi.org/10.1609/aaai.v37i7.25956.

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Conditional mean embedding (CME) operators encode conditional probability densities within Reproducing Kernel Hilbert Space (RKHS). In this paper, we present a decentralized algorithm for a collection of agents to cooperatively approximate CME over a network. Communication constraints limit the agents from sending all data to their neighbors; we only allow sparse representations of covariance operators to be exchanged among agents, compositions of which defines CME. Using a coherence-based compression scheme, we present a consensus-type algorithm that preserves the average of the approximation

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Segera, Davies, Mwangi Mbuthia, and Abraham Nyete. "Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis." BioMed Research International 2019 (December 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/4085725.

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Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support

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Muralinath, Rashmi N., Vishwambhar Pathak, and Prabhat K. Mahanti. "Metastable Substructure Embedding and Robust Classification of Multichannel EEG Data Using Spectral Graph Kernels." Future Internet 17, no. 3 (2025): 102. https://doi.org/10.3390/fi17030102.

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Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures across spatio-temporal-spectral dimensions. This study applies the graph Koopman embedding kernels (GKKE) method to extract latent neuro-markers of seizures from epileptiform EEG activity. EEG-derived graphs were constructed using correlati

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Wang, Yufan, Zijing Wang, Kai Ming Ting, and Yuanyi Shang. "A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection." Journal of Artificial Intelligence Research 79 (March 13, 2024): 865–93. http://dx.doi.org/10.1613/jair.1.15849.

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This paper aims to solve two enduring challenges in existing trajectory similarity measures: computational inefficiency and the absence of the ‘uniqueness’ property that should be guaranteed in a distance function: dist(X, Y ) = 0 if and only if X = Y , where X and Y are two trajectories. In this work, we present a novel approach utilizing a distributional kernel for trajectory representation and similarity measurement, based on the kernel mean embedding framework. It is the very first time a distributional kernel is used for trajectory representation and similarity measurement. Our method doe

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Brandman, David M., Michael C. Burkhart, Jessica Kelemen, Brian Franco, Matthew T. Harrison, and Leigh R. Hochberg. "Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression." Neural Computation 30, no. 11 (2018): 2986–3008. http://dx.doi.org/10.1162/neco_a_01129.

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Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities—specifically, the changes in the statistics of the data between training and testing data sets. This includes changes to the neural tuning profiles and baseline shifts in firing rates of recorded neurons, as well as nonphysiological noise. While progress has been made toward providing long-term user control via decoder recalibration, relatively little work has b

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Shi, Zhenbo, Xiaoman Liu, Yuxuan Zhang, et al. "Stop Diverse OOD Attacks: Knowledge Ensemble for Reliable Defense." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20436–44. https://doi.org/10.1609/aaai.v39i19.34251.

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Enhancing defense through model ensemble is an emerging trend, where the challenge lies in how to use ensemble knowledge to counter Out-of-Distribution (OOD) attacks. In this paper, we propose the Reliable Defense Ensemble (REE) to address this issue. REE optimizes the ensemble knowledge of models through aggregation and enhances multidimensional robust performance through collaboration. It employs the Dynamic Synergy Amplification for weight allocation and strategy adjustment. Furthermore, we design a new Kernel Anomaly Smoothing Detection Module, which detects anomalous attacks using a smoot

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Ali, Sarwan, and Murray Patterson. "Improving ISOMAP Efficiency with RKS: A Comparative Study with t-Distributed Stochastic Neighbor Embedding on Protein Sequences." J 6, no. 4 (2023): 579–91. http://dx.doi.org/10.3390/j6040038.

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Data visualization plays a crucial role in gaining insights from high-dimensional datasets. ISOMAP is a popular algorithm that maps high-dimensional data into a lower-dimensional space while preserving the underlying geometric structure. However, ISOMAP can be computationally expensive, especially for large datasets, due to the computation of the pairwise distances between data points. The motivation behind this study is to improve efficiency by leveraging an approximate method, which is based on random kitchen sinks (RKS). This approach provides a faster way to compute the kernel matrix. Usin

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Zhang, Hansong, Shikun Li, Pengju Wang, Dan Zeng, and Shiming Ge. "M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9314–22. http://dx.doi.org/10.1609/aaai.v38i8.28784.

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Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in substantial training and storage costs. To address these challenges, dataset condensation has been developed to learn a small synthetic set that preserves essential information from the original large-scale dataset. Nowadays, optimization-oriented methods have been the primary method in the field of dataset condensation for achieving SOTA results. However, the bi-level optimization process hinders the practical application of such methods to realistic and larger datasets. To enhance condensation efficiency

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Solodukha, Roman. "STATISTICAL STEGANALYSIS OF PHOTOREALISTIC IMAGES USING GRADIENT PATHS." Voprosy kiberbezopasnosti, no. 1(47) (2022): 26–36. http://dx.doi.org/10.21681/2311-3456-2022-1-26-36.

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Purpose of the article is experimentally test the efficiency of the feature vector based on gradient paths in the spatial domain of the image. Research method is a comparison of steganalytical feature vectors based on the mean square error and the coefficient of determination obtained using SVM-regression in Matlab. The dataset is formed by automating the freeware steganoprograms that implement embedding into the spatial area of the image with sequential and pseudorandom selection of a pixel for embedding. Results of the study: the optimal parameters of the algorithm for seeking gradient paths

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Ting, Kai Ming, Zongyou Liu, Hang Zhang, and Ye Zhu. "A new distributional treatment for time series and an anomaly detection investigation." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2321–33. http://dx.doi.org/10.14778/3551793.3551796.

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Time series is traditionally treated with two main approaches, i.e., the time domain approach and the frequency domain approach. These approaches must rely on a sliding window so that time-shift versions of a periodic subsequence can be measured to be similar. Coupled with the use of a root point-to-point measure, existing methods often have quadratic time complexity. We offer the third R domain approach. It begins with an insight that subsequences in a periodic time series can be treated as sets of independent and identically distributed (iid) points generated from an unknown distribution in

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Qian, Hangwei, Sinno Jialin Pan, and Chunyan Miao. "Distribution-Based Semi-Supervised Learning for Activity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7699–706. http://dx.doi.org/10.1609/aaai.v33i01.33017699.

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Supervised learning methods have been widely applied to activity recognition. The prevalent success of existing methods, however, has two crucial prerequisites: proper feature extraction and sufficient labeled training data. The former is important to differentiate activities, while the latter is crucial to build a precise learning model. These two prerequisites have become bottlenecks to make existing methods more practical. Most existing feature extraction methods highly depend on domain knowledge, while labeled data requires intensive human annotation effort. Therefore, in this paper, we pr

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19

Huang, Shimeng, Elisabeth Ailer, Niki Kilbertus, and Niklas Pfister. "Supervised learning and model analysis with compositional data." PLOS Computational Biology 19, no. 6 (2023): e1011240. http://dx.doi.org/10.1371/journal.pcbi.1011240.

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Supervised learning, such as regression and classification, is an essential tool for analyzing modern high-throughput sequencing data, for example in microbiome research. However, due to the compositionality and sparsity, existing techniques are often inadequate. Either they rely on extensions of the linear log-contrast model (which adjust for compositionality but cannot account for complex signals or sparsity) or they are based on black-box machine learning methods (which may capture useful signals, but lack interpretability due to the compositionality). We propose KernelBiome, a kernel-based

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Bie, Mei, Huan Xu, Quanle Liu, Yan Gao, Kai Song, and Xiangjiu Che. "DA-FER: Domain Adaptive Facial Expression Recognition." Applied Sciences 13, no. 10 (2023): 6314. http://dx.doi.org/10.3390/app13106314.

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Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address this issue, a domain adaptive learning strategy is proposed in this paper. The approach uses a public dataset with sufficient samples as the source domain and a small sample dataset as the target domain. Furthermore, the maximum mean discrepancy with kernel mean embedding is utilized to reduce the disparity betwe

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Ji, Bo-Ya, Liang-Rui Pan, Ji-Ren Zhou, Zhu-Hong You, and Shao-Liang Peng. "SMMDA: Predicting miRNA-Disease Associations by Incorporating Multiple Similarity Profiles and a Novel Disease Representation." Biology 11, no. 5 (2022): 777. http://dx.doi.org/10.3390/biology11050777.

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Increasing evidence has suggested that microRNAs (miRNAs) are significant in research on human diseases. Predicting possible associations between miRNAs and diseases would provide new perspectives on disease diagnosis, pathogenesis, and gene therapy. However, considering the intrinsic time-consuming and expensive cost of traditional Vitro studies, there is an urgent need for a computational approach that would allow researchers to identify potential associations between miRNAs and diseases for further research. In this paper, we presented a novel computational method called SMMDA to predict po

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22

Harris, Matthew. "KLRfome - Kernel Logistic Regression on Focal Mean Embeddings." Journal of Open Source Software 4, no. 35 (2019): 722. http://dx.doi.org/10.21105/joss.00722.

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23

De Cannière, Hélène, Federico Corradi, Christophe J. P. Smeets, et al. "Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation." Sensors 20, no. 12 (2020): 3601. http://dx.doi.org/10.3390/s20123601.

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Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the

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Hempel, John. "One-relator surface groups." Mathematical Proceedings of the Cambridge Philosophical Society 108, no. 3 (1990): 467–74. http://dx.doi.org/10.1017/s030500410006936x.

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For X a subset of a group G, the smallest normal subgroup of G which contains X is called the normal closure of X and is denoted by ngp (X; G) or simply by ngp (X) if there is no possibility of ambiguity. By a surface group we mean the fundamental group of a compact surface. We are interested in determining when a normal subgroup of a surface group contains a simple loop – the homotopy class of an embedding of S1 in the surface, or more generally, a power of a simple loop. This is significant to the study of 3-manifolds since a Heegaard splitting of a 3-manifold is reducible (cf. [2]) if and o

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Dong, Alice X. D., Jennifer S. K. Chan, and Gareth W. Peters. "RISK MARGIN QUANTILE FUNCTION VIA PARAMETRIC AND NON-PARAMETRIC BAYESIAN APPROACHES." ASTIN Bulletin 45, no. 3 (2015): 503–50. http://dx.doi.org/10.1017/asb.2015.8.

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AbstractWe develop quantile functions from regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk margin and an overview of implied capital based on the historical volatility of a general insurers loss portfolio. Two modeling frameworks are considered based around parametric and non-parametric regression models which we develop specifically in this insura

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Saito, Shota. "Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel k-Means, and Heat Kernel." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 8141–49. http://dx.doi.org/10.1609/aaai.v36i7.20787.

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We propose a theoretical framework of multi-way similarity to model real-valued data into hypergraphs for clustering via spectral embedding. For graph cut based spectral clustering, it is common to model real-valued data into graph by modeling pairwise similarities using kernel function. This is because the kernel function has a theoretical connection to the graph cut. For problems where using multi-way similarities are more suitable than pairwise ones, it is natural to model as a hypergraph, which is generalization of a graph. However, although the hypergraph cut is well-studied, there is not

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Zang, Xian, and Kil To Chong. "Embedding Global Optimization and Kernelization into Fuzzy C-Means Clustering for Consonant/Vowel Segmentation." Applied Mechanics and Materials 419 (October 2013): 814–19. http://dx.doi.org/10.4028/www.scientific.net/amm.419.814.

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This paper proposes a novel clustering algorithm named global kernel fuzzy-c means (GK-FCM) to segment the speech into small non-overlapping blocks for consonant/vowel segmentation. This algorithm is realized by embedding global optimization and kernelization into the classical fuzzy c-means clustering algorithm. It proceeds in an incremental way attempting to optimally add new cluster center at each stage through the kernel-based fuzzy c-means. By solving all the intermediate problems, the final near-optimal solution is determined in a deterministic way. This algorithm overcomes the well-know

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Kanagawa, Motonobu, Yu Nishiyama, Arthur Gretton, and Kenji Fukumizu. "Filtering with State-Observation Examples via Kernel Monte Carlo Filter." Neural Computation 28, no. 2 (2016): 382–444. http://dx.doi.org/10.1162/neco_a_00806.

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This letter addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e., the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowledge of the observation model is provided only by examples of state-observation pairs. This setting is important and appears when state variables are defined as quantities that are very different from the observations. We propose kernel Monte Carlo filter, a novel filtering met

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Zhang, Yi, Jie Lu, Feng Liu, et al. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding." Journal of Informetrics 12, no. 4 (2018): 1099–117. http://dx.doi.org/10.1016/j.joi.2018.09.004.

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Tamas, Ambrus, and Balazs Csanad Csaji. "Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings." IEEE Control Systems Letters 6 (2022): 860–65. http://dx.doi.org/10.1109/lcsys.2021.3087409.

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Cong, Zhang, Zeng Shan, and Zhang Hui. "Electrocardiography Classification Based on Revised Locally Linear Embedding Algorithm and Kernel-Based Fuzzy C-Means Clustering." Journal of Medical Imaging and Health Informatics 4, no. 6 (2014): 916–21. http://dx.doi.org/10.1166/jmihi.2014.1342.

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Yasemin, Atayolu*, and Kutlu Yakup. "Effective Use of Content as a Feature in IMDB Dataset Analysis." Journal of Artificial Intelligence with Applications 4, no. 1 (2023): 1–6. https://doi.org/10.5281/zenodo.14587323.

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This study uses data from IMDb and TMDB to build two machine learning models. One model predicts movie ratings, and the other classifies movie genres. To predict ratings, we work with a carefully selected subset of IMDb data, including details like titles, genres, ratings, and crew roles, to create a structured dataset. We focus on Gradient Boosting Decision Trees (GBDT), including XGBoost and CatBoost models, and check their performance with metrics like Mean Squared Error (MSE) and Mean Absolute Error (MAE). Out of the tested models, the Gradient Boosting Regressor gives the best results, ac

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Liu, Yang, Jiayun Tian, Xuemei Liu, et al. "Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering." Electronics 12, no. 14 (2023): 3099. http://dx.doi.org/10.3390/electronics12143099.

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In response to the shortcomings of existing knowledge graph embedding strategies, such as weak feature interaction and latent knowledge representation, a unique hydraulic knowledge graph embedding method is suggested. The proposed method incorporates spatial position features into the entity-relation embedding process, thereby enhancing the representation capability of latent knowledge. Furthermore, it utilizes a multi-layer convolutional neural network to fuse features at different levels, effectively capturing more abundant semantic information. Additionally, the method employs multi-scale d

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Tschan-Plessl, Astrid, Eivind Heggernes Ask, Thea Johanne Gjerdingen, et al. "System-Level Disease-Driven Immune Signatures in Patients with Diffuse Large B-Cell Lymphoma Associated with Poor Survival." Blood 134, Supplement_1 (2019): 2897. http://dx.doi.org/10.1182/blood-2019-131359.

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Global gene expression profiling of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) has revealed broad innate immune signatures that distinguish the heterogeneous disease subtypes and correlate with good treatment outcome. However, we still lack tools to identify the relatively large group of patients that are refractory to initial therapy and have a dismal prognosis. Here, we used mass cytometry and serum profiling in a systems-level approach to analyze immune responses in 36 patients with aggressive B cell lymphoma and age- and sex-matched healthy controls. Stochastic nei

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Huang, Jingxu, Qiong Liu, Lang Xiang, Guangrui Li, Yiqing Zhang, and Wenbai Chen. "A Lightweight Residual Model for Corrosion Segmentation with Local Contextual Information." Applied Sciences 12, no. 18 (2022): 9095. http://dx.doi.org/10.3390/app12189095.

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Metal corrosion in high-risk areas, such as high-altitude cables and chemical factories, is very complex and inaccessible to people, which can be a hazard and compromise people’s safety. Embedding deep learning models into edge computing devices is urgently needed to conduct corrosion inspections. However, the parameters of current state-of-the-art models are too large to meet the computation and storage requirements of mobile devices, while lightweight models perform poorly in complex corrosion environments. To address these issues, a lightweight residual deep-learning model based on an encod

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Kusaba, Minoru, Yoshihiro Hayashi, Chang Liu, Araki Wakiuchi, and Ryo Yoshida. "Representation of materials by kernel mean embedding." Physical Review B 108, no. 13 (2023). http://dx.doi.org/10.1103/physrevb.108.134107.

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Wu, Xi-Zhu, Wenkai Xu, Song Liu, and Zhi-Hua Zhou. "Model Reuse with Reduced Kernel Mean Embedding Specification." IEEE Transactions on Knowledge and Data Engineering, 2021, 1. http://dx.doi.org/10.1109/tkde.2021.3086619.

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Guo, Liping, Jimin Wang, Yanlong Zhao, and Ji-Feng Zhang. "Consensus-Based Distributed Nonlinear Filtering With Kernel Mean Embedding." IEEE Transactions on Aerospace and Electronic Systems, 2024, 1–16. https://doi.org/10.1109/taes.2024.3513280.

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Li, Guofa, Zefeng Ji, Yunlong Chang, Shen Li, Xingda Qu, and Dongpu Cao. "ML-ANet: A Transfer Learning Approach Using Adaptation Network for Multi-label Image Classification in Autonomous Driving." Chinese Journal of Mechanical Engineering 34, no. 1 (2021). http://dx.doi.org/10.1186/s10033-021-00598-9.

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AbstractTo reduce the discrepancy between the source and target domains, a new multi-label adaptation network (ML-ANet) based on multiple kernel variants with maximum mean discrepancies is proposed in this paper. The hidden representations of the task-specific layers in ML-ANet are embedded in the reproducing kernel Hilbert space (RKHS) so that the mean-embeddings of specific features in different domains could be precisely matched. Multiple kernel functions are used to improve feature distribution efficiency for explicit mean embedding matching, which can further reduce domain discrepancy. Ad

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Alyakin, Anton A., Joshua Agterberg, Hayden S. Helm, and Carey E. Priebe. "Correcting a nonparametric two-sample graph hypothesis test for graphs with different numbers of vertices with applications to connectomics." Applied Network Science 9, no. 1 (2024). http://dx.doi.org/10.1007/s41109-023-00607-x.

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AbstractRandom graphs are statistical models that have many applications, ranging from neuroscience to social network analysis. Of particular interest in some applications is the problem of testing two random graphs for equality of generating distributions. Tang et al. (Bernoulli 23:1599–1630, 2017) propose a test for this setting. This test consists of embedding the graph into a low-dimensional space via the adjacency spectral embedding (ASE) and subsequently using a kernel two-sample test based on the maximum mean discrepancy. However, if the two graphs being compared have an unequal number

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Hayati, Saeed, Kenji Fukumizu, and Afshin Parvardeh. "Kernel Mean Embedding of Probability Measures and its Applications to Functional Data Analysis." Scandinavian Journal of Statistics, October 12, 2023. http://dx.doi.org/10.1111/sjos.12691.

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AbstractThis study intends to introduce kernel mean embedding of probability measures over infinite‐dimensional separable Hilbert spaces induced by functional response statistical models. The embedded function represents the concentration of probability measures in small open neighborhoods, which identifies a pseudo‐likelihood and fosters a rich framework for statistical inference. Utilizing Maximum Mean Discrepancy, we devise new tests in functional response models. The performance of new derived tests is evaluated against competitors in three major problems in functional data analysis includ

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Gual-Arnau, Ximo, and Juan Monterde. "Enhancing a Kernel Method for Shape Analysis in Kendall Space." Journal of Mathematical Imaging and Vision 67, no. 2 (2025). https://doi.org/10.1007/s10851-025-01235-z.

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Abstract In learning methods defined on a manifold, it is often important to embed the manifold into a Hilbert space to apply kernel methods, as it enables the utilization of linear techniques in the transformed space, thereby facilitating complex data analysis and classification tasks with nonlinear structures [10]. In [6], the authors define an embedding of the $$\textit{m}$$ m -dimensional Kendall shape space into an $$\mathbb {R}^{\textit{N}}$$ R N space, from which kernel methods are introduced in the Kendall shape space. In this paper, we significantly simplify this result by achieving a

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Das, Srinjoy, Hrushikesh N. Mhaskar, and Alexander Cloninger. "Kernel Distance Measures for Time Series, Random Fields and Other Structured Data." Frontiers in Applied Mathematics and Statistics 7 (December 22, 2021). http://dx.doi.org/10.3389/fams.2021.787455.

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This paper introduces kdiff, a novel kernel-based measure for estimating distances between instances of time series, random fields and other forms of structured data. This measure is based on the idea of matching distributions that only overlap over a portion of their region of support. Our proposed measure is inspired by MPdist which has been previously proposed for such datasets and is constructed using Euclidean metrics, whereas kdiff is constructed using non-linear kernel distances. Also, kdiff accounts for both self and cross similarities across the instances and is defined using a lower

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Wynne, George, and Stanislav Nagy. "Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis." International Statistical Review, March 16, 2025. https://doi.org/10.1111/insr.12611.

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SummaryStatistical depth is the act of gauging how representative a point is compared with a reference probability measure. The depth allows introducing rankings and orderings to data living in multivariate, or function spaces. Though widely applied and with much experimental success, little theoretical progress has been made in analysing functional depths. This article highlights how the common ‐depth and related depths from functional data analysis can be viewed as a kernel mean embedding, widely used in statistical machine learning. This facilitates answers to several open questions regardi

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García Meixide, Carlos, and Marcos Matabuena. "Causal survival embeddings: Non-parametric counterfactual inference under right-censoring." Statistical Methods in Medical Research, February 11, 2025. https://doi.org/10.1177/09622802241311455.

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Counterfactual inference at the distributional level presents new challenges with censored targets, especially in modern healthcare problems. To mitigate selection bias in this context, we exploit the intrinsic structure of reproducing kernel Hilbert spaces (RKHS) harnessing the notion of kernel mean embedding. This enables the development of a non-parametric estimator of counterfactual survival functions. We provide rigorous theoretical guarantees regarding consistency and convergence rates of our new estimator under general hypotheses related to smoothness of the underlying RKHS. We illustra

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Alquier, P., and M. Gerber. "Universal Robust Regression via Maximum Mean Discrepancy." Biometrika, May 10, 2023. http://dx.doi.org/10.1093/biomet/asad031.

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Abstract Many modern datasets are collected automatically and are thus easily contaminated by outliers. This has led to a renewed interest in robust estimation, including new notions of robustness such as robustness to adversarial contamination of the data. However, most robust estimation methods are designed for a specific model. Notably, many methods were proposed recently to obtain robust estimators in linear models, or generalized linear models, and a few were developed for very specific settings, for example beta regression or sample selection models. In this paper we develop a new approa

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Jarne, Cecilia, Ben Griffin, and Diego Vidaurre. "Predicting Subject Traits From Brain Spectral Signatures: An Application to Brain Ageing." Human Brain Mapping 45, no. 18 (2024). https://doi.org/10.1002/hbm.70096.

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ABSTRACTThe prediction of subject traits using brain data is an important goal in neuroscience, with relevant applications in clinical research, as well as in the study of differential psychology and cognition. While previous prediction work has predominantly been done on neuroimaging data, our focus is on electroencephalography (EEG), a relatively inexpensive, widely available and non‐invasive data modality. However, EEG data is complex and needs some form of feature extraction for subsequent prediction. This process is sometimes done manually, risking biases and suboptimal decisions. Here we

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Wu, Jiawei, Teng Wang, Lijun Yang, and Jingxuan Li. "Extrapolation method of flame nonlinear thermoacoustic response in the time domain based on the kernel embedding of conditional distribution." Physics of Fluids 37, no. 3 (2025). https://doi.org/10.1063/5.0256838.

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In this study, we propose a novel method for extrapolating the nonlinear thermoacoustic response of flames in the time domain, based on kernel embedding of conditional distribution (KECD). This approach enables the prediction of full-flame nonlinear responses under high-amplitude oncoming flow disturbances (target domain) using the responses under low-amplitude perturbations (source domain) and a limited dataset of pure-tone flame responses under high-amplitude perturbations. The KECD loss term plays a key role in constraining the neural network to map flame nonlinear responses that are consis

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Gachon, Erell, Jérémie Bigot, Elsa Cazelles, et al. "Low Dimensional Representation of Multi‐Patient Flow Cytometry Datasets Using Optimal Transport for Measurable Residual Disease Detection in Leukemia." Cytometry Part A, March 3, 2025. https://doi.org/10.1002/cyto.a.24918.

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ABSTRACTRepresenting and quantifying Measurable Residual Disease (MRD) in Acute Myeloid Leukemia (AML), a type of cancer that affects the blood and bone marrow, is essential in the prognosis and follow‐up of AML patients. As traditional cytological analysis cannot detect leukemia cells below 5%, the analysis of flow cytometry datasets is expected to provide more reliable results. In this paper, we explore statistical learning methods based on optimal transport (OT) to achieve a relevant low‐dimensional representation of multi‐patient flow cytometry measurements (FCM) datasets considered as hig

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HUNG, MING HUI, Chun-Hung Chen, Yu-Hsuan Tseng, and Chin-Chou Huang. "Abstract 4145524: Artificial Intelligence-Based Screening for Blood Pressure Phenotypes of White-coat and Masked Hypertension in Outpatient Settings." Circulation 150, Suppl_1 (2024). http://dx.doi.org/10.1161/circ.150.suppl_1.4145524.

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Introduction: White-coat hypertension (WCH) and masked hypertension (MH) complicate accurate blood pressure (BP) monitoring. While ambulatory BP monitoring (ABPM) is effective, its high cost and limited availability are significant barriers. Hypothesis: We hypothesized that a machine learning (ML) model using clinical data from a single outpatient visit could accurately predict WCH and MH. Aims: This study aimed to develop and validate ML-based prediction models for WCH and MH using accessible clinical data to improve diagnostic efficiency and accessibility. Methods: We enrolled patients from

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