We develop an optimization algorithm suitable for Bayesian learning in complex models. Our approach relies on natural gradient updates within a general black-box framework for efficient training with limited model-specific derivations. It applies within the class of exponential-family variational posterior distributions, for which we extensively discuss the Gaussian case, for which the updates have a rather simple form. Our Quasi Black-box Variational Inference (QBVI) framework is readily applicable to a wide class of Bayesian inference problems and is of simple implementation as the updates of the variational posterior do not involve gradients with respect to the model parameters, nor the prescription of the Fisher information matrix. We develop QBVI under different hypotheses for the posterior covariance matrix, discuss details about its robust and feasible implementation, and provide a number of real-world applications to demonstrate its effectiveness.
Incomplete datasets, in which some objects contain missing entries in certain dimensions, are pervasive in real-world applications. Most existing clustering methods for incomplete data adopt a two-stage pipeline: first imputing missing entries and then performing clustering. However, both imputation and clustering procedures typically involve multiple hyperparameters, which substantially increases the difficulty of producing reliable clustering results in practice. Although decision-graph-based approaches have been shown to alleviate parameter dependence, existing formulations generally assume that all objects share the same set of observed dimensions, and thus cannot be directly applied to incomplete datasets.To address these limitations, we propose a Single-Dimensional Clustering algorithm (SDC), a parameter-free clustering framework tailored for incomplete datasets. By eliminating the imputation stage and extending decision graphs to incomplete data through dimension splitting and a “partition intersection” fusion mechanism, SDC enables effective clustering without requiring any user-specified parameters. Experimental results demonstrate that, across three evaluation metrics, SDC outperforms baseline algorithms by at least 13.7% (NMI), 23.8% (ARI), and 8.1% (Purity). The source code is publicly available at https://github.com/DJLPKU/single_dimension_clustering.
Domain Adaptation (DA) has made substantial progress in mitigating domain shift across diverse domains. However, many unsupervised DA methods rely on explicit cross-domain feature alignment, which can distort target domain features in the absence of ground-truth labels. In this paper, we propose a Target Self-Guided Framework (TSGF), which strengthens the guidance of the target domain for knowledge transfer, achieving adaptation without explicit cross-domain feature alignment, thereby mitigating the feature distortion problem in the target domain. In TSGF, the target domain offers both instance-level and class-level guidance through contrastive learning and pseudo-labeling, respectively, enhancing adaptation through target domain-specific supervision. Furthermore, a patch-mix strategy is introduced to bridge the domain gap, thereby facilitating the capture of shared knowledge across domains. As the emerging Mamba architecture possesses global receptive fields and superior linear complexity compared to Convolutional Neural Network (CNN) and Vision Transformer (ViT) architectures, we explore the application of the Mamba architecture to domain adaptation. Extensive experiments demonstrate that our method achieves superior performance over state-of-the-art approaches, effectively bridging the source and target domains in challenging DA scenarios.
Given the immutable physiological nature of biometric characteristics and their widespread technological adoption, the development of robust template protection mechanisms has become imperative for biometric systems. However, conventional template transformation methodologies frequently encounter critical limitations: performance degradation, cross-domain instability, and residual information leakage - deficiencies that simultaneously compromise system security and recognition efficacy. This paper proposes a paradigm shift through re-engineering of traditional biometric recognition frameworks, specifically leveraging their training-phase models to develop HKNet (Hybrid Key-set Network). Our novel architecture establishes a hybrid key ensemble where each constituent key encapsulates partial reference information through optimized parameter sharing, and performs segmented feature matching via a cross-attention weight matrix. This enables effective decoupling of one-to-one mapping relationships, ensuring that adversaries cannot infer specific user identities through individual key analysis. Furthermore, we propose a key learning and optimization strategy that integrated by tripartite loss function integrates: auxiliary weighted disparity loss for discriminative feature separation, feature discretization loss for security and discrimination, and distance constraint loss for optimal key-space distribution. Through comprehensive experimentation on five public palm vein datasets, the proposed framework demonstrates not only surpasses existing encrypted-domain methods, but also comparable performance to state-of-the-art non-encrypted systems, and formal security guarantees meeting ISO/IEC 24,745 standards for irreversibility, unlinkability, and revocability. We release our code at https://github.com/7033486/HKNet.
Anchor-based multi-view clustering has gained considerable attention owing to its efficiency in dealing with large-scale data by jointly learning compact anchors and structural anchor graph within a unified framework. Most existing methods mainly focus on improving clustering by prior structural constraints on anchor graph and ignore the explicit structural semantic exploration for anchors, which may be insufficient to ensure that anchors are truly representative and limits their expressiveness in discriminative anchor graph learning. To this end, this study proposes Anchor-to-Graph Structural Co-regularization for Multi-view Clustering (AGSCR-MVC), which collaboratively promotes the structural semantic exploration for anchor and anchor graph in a unified framework. In detail, the AGSCR-MVC introduces bipartite graph Laplacian regularizer to improve the clustering structure of anchor graph. And then, it adaptively constructs the anchor adjacency relationships from anchor graph and enforces the explicit structure of latent anchors. The structural regularization from anchors to anchor graph could be negotiated with each other, collaboratively promoting anchor discrimination and clustering quality. Furthermore, the log-sum sparsity is imposed on anchor graph to enhance local structural information. An efficient optimization algorithm is developed for solving the formulated objective problem of proposed model. Extensive experiments persuasively confirm that AGSCR-MVC achieves a compromise superiority in computational complexity and clustering performance. The source code of AGSCR-MVC could be available at https://github.com/XX106222/AGSCR-MVC.
