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Related Experiment Video

Updated: Apr 16, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

A robust multi-view support vector machine with the RoBoSS loss function.

Yash Arora¹, S K Gupta¹, M Tanveer²

¹Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India.

Neural Networks : the Official Journal of the International Neural Network Society

|April 14, 2026

Summary

This summary is machine-generated.

This study introduces RoBoSS-MvSVM, a robust multi-view support vector machine (SVM) that integrates consensus and complementary information. It effectively handles noisy data and outperforms existing methods in multi-view learning tasks.

Keywords:

Consensus and complementarity principles Multi-view learning RoBoSS loss function Support vector machines

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Last Updated: Apr 16, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Area of Science:

Machine Learning
Data Science
Computer Vision

Background:

Multi-view learning leverages multiple data representations for improved performance.
Existing support vector machine (SVM)-based multi-view models often neglect complementary information and lack robustness to noise and inconsistencies.
There is a need for robust multi-view learning methods that can effectively integrate diverse data representations.

Purpose of the Study:

To propose a novel robust multi-view SVM framework, RoBoSS-MvSVM, that addresses limitations of existing methods.
To explicitly integrate both consensus and complementary information across multiple views.
To enhance the robustness and generalization performance of multi-view learning.

Main Methods:

Developed a robust multi-view SVM framework (RoBoSS-MvSVM) utilizing the RoBoSS loss function.
The RoBoSS loss function is designed for robustness, boundedness, sparsity, and smoothness, handling noisy and inconsistent samples.
Optimization is performed using the Nesterov accelerated gradient algorithm, with generalization capacity analyzed via Rademacher complexity.

Main Results:

RoBoSS-MvSVM demonstrated superior performance compared to baseline methods across synthetic, UCI/KEEL, and Animal with Attribute datasets.
The proposed method effectively integrates consensus and complementary information, leading to enriched data representation and resilient learning.
Experimental results consistently showed the outperformance of RoBoSS-MvSVM, with stability confirmed by hyperparameter analysis.

Conclusions:

RoBoSS-MvSVM offers a robust and effective solution for multi-view learning by integrating diverse information and handling data imperfections.
The framework provides reliable generalization performance, validated through theoretical analysis and comprehensive experiments.
The proposed method represents a significant advancement in robust multi-view SVM techniques.