Subspace Kernel Learning on Tensor Sequences

• URL: http://arxiv.org/abs/2603.19546v1
• Date: Fri, 20 Mar 2026 01:04:30 GMT
• Title: Subspace Kernel Learning on Tensor Sequences
• Abstract summary: We introduce Uncertainty-driven Kernel Learning (UKTL), a novel kernel framework for $M$-mode tensors.<n>UKTL achieves state-of-the-art performance, superior generalization, and meaningful mode-wise insights.<n>This work establishes a principled, scalable, and interpretable kernel learning paradigm for structured multi-way and multi-modal tensor sequences.
• Score: 44.358377952850994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning from structured multi-way data, represented as higher-order tensors, requires capturing complex interactions across tensor modes while remaining computationally efficient. We introduce Uncertainty-driven Kernel Tensor Learning (UKTL), a novel kernel framework for $M$-mode tensors that compares mode-wise subspaces derived from tensor unfoldings, enabling expressive and robust similarity measure. To handle large-scale tensor data, we propose a scalable Nyström kernel linearization with dynamically learned pivot tensors obtained via soft $k$-means clustering. A key innovation of UKTL is its uncertainty-aware subspace weighting, which adaptively down-weights unreliable mode components based on estimated confidence, improving robustness and interpretability in comparisons between input and pivot tensors. Our framework is fully end-to-end trainable and naturally incorporates both multi-way and multi-mode interactions through structured kernel compositions. Extensive evaluations on action recognition benchmarks (NTU-60, NTU-120, Kinetics-Skeleton) show that UKTL achieves state-of-the-art performance, superior generalization, and meaningful mode-wise insights. This work establishes a principled, scalable, and interpretable kernel learning paradigm for structured multi-way and multi-modal tensor sequences.

Related papers

• KVNN: Learnable Multi-Kernel Volterra Neural Networks [6.550684951976901]
  kernelized Volterra Neural Network (kVNN)<n>Experiments on two representative tasks: video action recognition and image denoising.<n>Results are maintained even when trained from scratch without large-scale pretraining.
  arXiv  Detail & Related papers  (2026-04-16T15:18:48Z)
• Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering [2.538209532048867]
  We present a robust personalized learning framework for multi-view fuzzy c-means clustering.<n>Our approach integrates heat- kernel coefficients adapted from quantum field theory with Tucker decomposition and canonical polyadic decomposition.
  arXiv  Detail & Related papers  (2025-09-19T15:45:02Z)
• Score-Based Model for Low-Rank Tensor Recovery [49.158601255093416]
  Low-rank tensor decompositions (TDs) provide an effective framework for multiway data analysis.<n>Traditional TD methods rely on predefined structural assumptions, such as CP or Tucker decompositions.<n>We propose a score-based model that eliminates the need for predefined structural or distributional assumptions.
  arXiv  Detail & Related papers  (2025-06-27T15:05:37Z)
• Tensor Convolutional Network for Higher-Order Interaction Prediction in Sparse Tensors [74.31355755781343]
  We propose TCN, an accurate and compatible tensor convolutional network that integrates seamlessly with TF methods for predicting top-k interactions.<n>We show that TCN integrated with a TF method outperforms competitors, including TF methods and a hyperedge prediction method.
  arXiv  Detail & Related papers  (2025-03-14T18:22:20Z)
• Adaptively Topological Tensor Network for Multi-view Subspace Clustering [36.790637575875635]
  Multi-view subspace clustering uses learned self-representation tensors to exploit low rank information. A pre-defined tensor decomposition may not fully exploit low rank information for a certain dataset. We propose the adaptively topological tensor network (ATTN) by determining the edge ranks from the structural information of the self-representation tensor.
  arXiv  Detail & Related papers  (2023-05-01T08:28:33Z)
• Multi-View Clustering via Semi-non-negative Tensor Factorization [120.87318230985653]
  We develop a novel multi-view clustering based on semi-non-negative tensor factorization (Semi-NTF) Our model directly considers the between-view relationship and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point.
  arXiv  Detail & Related papers  (2023-03-29T14:54:19Z)
• Estimating Higher-Order Mixed Memberships via the $\ell_{2,\infty}$ Tensor Perturbation Bound [8.521132000449766]
  We propose the tensor mixed-membership blockmodel, a generalization of the tensor blockmodel. We establish the identifiability of our model and propose a computationally efficient estimation procedure. We apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships.
  arXiv  Detail & Related papers  (2022-12-16T18:32:20Z)
• Tensor Full Feature Measure and Its Nonconvex Relaxation Applications to Tensor Recovery [1.8899300124593645]
  We propose a new tensor sparsity measure called Full Feature Measure (FFM) It can simultaneously describe the feature dimension each dimension, and connect the Tucker rank with the tensor tube rank. Two efficient models based on FFM are proposed, and two Alternating Multiplier Method (ADMM) algorithms are developed to solve the proposed model.
  arXiv  Detail & Related papers  (2021-09-25T01:44:34Z)
• Tensor Representations for Action Recognition [54.710267354274194]
  Human actions in sequences are characterized by the complex interplay between spatial features and their temporal dynamics. We propose novel tensor representations for capturing higher-order relationships between visual features for the task of action recognition. We use higher-order tensors and so-called Eigenvalue Power Normalization (NEP) which have been long speculated to perform spectral detection of higher-order occurrences.
  arXiv  Detail & Related papers  (2020-12-28T17:27:18Z)
• Efficient Structure-preserving Support Tensor Train Machine [0.0]
  Train Multi-way Multi-level Kernel (TT-MMK) We develop the Train Multi-way Multi-level Kernel (TT-MMK), which combines the simplicity of the Polyadic decomposition, the classification power of the Dual Structure-preserving Support Machine, and the reliability of the Train Vector approximation. We show by experiments that the TT-MMK method is usually more reliable, less sensitive to tuning parameters, and gives higher prediction accuracy in the SVM classification when benchmarked against other state-of-the-art techniques.
  arXiv  Detail & Related papers  (2020-02-12T16:35:10Z)
• Supervised Learning for Non-Sequential Data: A Canonical Polyadic Decomposition Approach [85.12934750565971]
  Efficient modelling of feature interactions underpins supervised learning for non-sequential tasks. To alleviate this issue, it has been proposed to implicitly represent the model parameters as a tensor. For enhanced expressiveness, we generalize the framework to allow feature mapping to arbitrarily high-dimensional feature vectors.
  arXiv  Detail & Related papers  (2020-01-27T22:38:40Z)

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.