TRKM: Twin Restricted Kernel Machines for Classification and Regression

• URL: http://arxiv.org/abs/2502.15759v1
• Date: Thu, 13 Feb 2025 05:13:46 GMT
• Title: TRKM: Twin Restricted Kernel Machines for Classification and Regression
• Authors: A. Quadir, M. Tanveer,
• Abstract summary: TRKM combines the benefits of twin models with the robustness of the RKM framework to enhance classification and regression tasks.<n>We implement the TRKM model on the brain age dataset, demonstrating its efficacy in predicting brain age.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Restricted kernel machines (RKMs) have considerably improved generalization in machine learning. Recent advancements explored various techniques within the RKM framework, integrating kernel functions with least squares support vector machines (LSSVM) to mirror the energy function of restricted Boltzmann machines (RBM), leading to enhanced performance. However, RKMs may face challenges in generalization when dealing with unevenly distributed or complexly clustered data. Additionally, as the dataset size increases, the computational burden of managing high-dimensional feature spaces can become substantial, potentially hindering performance in large-scale datasets. To address these challenges, we propose twin restricted kernel machine (TRKM). TRKM combines the benefits of twin models with the robustness of the RKM framework to enhance classification and regression tasks. By leveraging the Fenchel-Young inequality, we introduce a novel conjugate feature duality, allowing the formulation of classification and regression problems in terms of dual variables. This duality provides an upper bound to the objective function of the TRKM problem, resulting in a new methodology under the RKM framework. The model uses an energy function similar to that of RBM, incorporating both visible and hidden variables corresponding to both classes. Additionally, the kernel trick is employed to map data into a high-dimensional feature space, where the model identifies an optimal separating hyperplane using a regularized least squares approach. Experiments on UCI and KEEL datasets confirm TRKM's superiority over baselines, showcasing its robustness and efficiency in handling complex data. Furthermore, We implemented the TRKM model on the brain age dataset, demonstrating its efficacy in predicting brain age.

Related papers

• CI-RKM: A Class-Informed Approach to Robust Restricted Kernel Machines [0.0]
  Restricted kernel machines (RKMs) represent a versatile and powerful framework within the kernel machine family. We propose a novel enhancement to the RKM framework by integrating a class-informed weighted function. Our proposed method establishes a significant advancement in the development of kernel-based learning models.
  arXiv  Detail & Related papers  (2025-04-12T11:12:30Z)
• Reinforced Model Merging [53.84354455400038]
  We present an innovative framework termed Reinforced Model Merging (RMM), which encompasses an environment and agent tailored for merging tasks. By utilizing data subsets during the evaluation process, we addressed the bottleneck in the reward feedback phase, thereby accelerating RMM by up to 100 times.
  arXiv  Detail & Related papers  (2025-03-27T08:52:41Z)
• Randomized based restricted kernel machine for hyperspectral image classification [0.0]
  Random vector functional link (RVFL) network has gained significant popularity in hyperspectral image (HSI) classification. RVFL models face several limitations, particularly in handling non-linear relationships and complex data structures. We propose a novel randomized based restricted kernel machine ($R2KM$) model that combines the strehyperngths of RVFL and restricted kernel machines.
  arXiv  Detail & Related papers  (2025-03-06T17:18:39Z)
• One Class Restricted Kernel Machines [0.0]
  Restricted kernel machines (RKMs) have demonstrated a significant impact in enhancing generalization ability in the field of machine learning.<n>RKMs's efficacy can be compromised by the presence of outliers and other forms of contamination within the dataset.<n>To address this critical issue and robustness of the model, we propose the novel one-class RKM (OCRKM)<n>In the framework of OCRKM, we employ an energy function akin to that of the RBM, which integrates both visible and hidden variables in a nonprobabilistic setting.
  arXiv  Detail & Related papers  (2025-02-11T07:11:20Z)
• HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks [7.06787067270941]
  The integration of hyperspectral imaging (HSI) and LiDAR data within new linear feature spaces offers a promising solution to the challenges posed by the high-dimensionality and redundancy inherent in HSIs.<n>This study introduces a dual linear fused space framework that capitalizes on bidirectional reversed convolutional neural network (CNN) pathways, coupled with a specialized spatial analysis block.<n>The proposed method not only enhances data processing and classification accuracy, but also mitigates the computational burden typically associated with advanced models such as Transformers.
  arXiv  Detail & Related papers  (2024-11-30T01:08:08Z)
• CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction [77.8576094863446]
  We propose a new detextbfCoupled dutextbfAl-interactive lineatextbfR atttextbfEntion (CARE) mechanism. We first propose an asymmetrical feature decoupling strategy that asymmetrically decouples the learning process for local inductive bias and long-range dependencies. By adopting a decoupled learning way and fully exploiting complementarity across features, our method can achieve both high efficiency and accuracy.
  arXiv  Detail & Related papers  (2024-11-25T07:56:13Z)
• Multiview learning with twin parametric margin SVM [0.0]
  Multiview learning (MVL) seeks to leverage the benefits of diverse perspectives to complement each other. We propose multiview twin parametric margin support vector machine (MvTPMSVM) MvTPMSVM constructs parametric margin hyperplanes corresponding to both classes, aiming to regulate and manage the impact of the heteroscedastic noise structure.
  arXiv  Detail & Related papers  (2024-08-04T10:16:11Z)
• Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs [65.42104819071444]
  Multitask learning (MTL) leverages task-relatedness to enhance performance. We employ high-order tensors, with each mode corresponding to a task index, to naturally represent tasks referenced by multiple indices. We propose a general framework of low-rank MTL methods with tensorized support vector machines (SVMs) and least square support vector machines (LSSVMs)
  arXiv  Detail & Related papers  (2023-08-30T14:28:26Z)
• Bayesian learning of feature spaces for multitasks problems [0.11538034264098687]
  This paper introduces a novel approach for multi-task regression that connects Kernel Machines (KMs) and Extreme Learning Machines (ELMs) The proposed models, termed RFF-BLR, stand on a Bayesian framework that simultaneously addresses two main design goals. The experimental results show that this framework can lead to significant performance improvements compared to the state-of-the-art methods in nonlinear regression.
  arXiv  Detail & Related papers  (2022-09-07T09:53:53Z)
• Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models [68.9288651177564]
  We present a novel MoE architecture based on matrix product operators (MPO) from quantum many-body physics. With the decomposed MPO structure, we can reduce the parameters of the original MoE architecture. Experiments on the three well-known downstream natural language datasets based on GPT2 show improved performance and efficiency in increasing model capacity.
  arXiv  Detail & Related papers  (2022-03-02T13:44:49Z)
• Learning Augmentation Distributions using Transformed Risk Minimization [47.236227685707526]
  We propose a new emphTransformed Risk Minimization (TRM) framework as an extension of classical risk minimization. As a key application, we focus on learning augmentations to improve classification performance with a given class of predictors.
  arXiv  Detail & Related papers  (2021-11-16T02:07:20Z)
• Cauchy-Schwarz Regularized Autoencoder [68.80569889599434]
  Variational autoencoders (VAE) are a powerful and widely-used class of generative models. We introduce a new constrained objective based on the Cauchy-Schwarz divergence, which can be computed analytically for GMMs. Our objective improves upon variational auto-encoding models in density estimation, unsupervised clustering, semi-supervised learning, and face analysis.
  arXiv  Detail & Related papers  (2021-01-06T17:36:26Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality [11.68800227521015]
  We introduce weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs) The RKM formulation allows for an easy integration of methods from classical robust statistics. Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data.
  arXiv  Detail & Related papers  (2020-02-04T09:23:25Z)

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.