Tensor analysis models for high-dimensional computer vision tasks
Srikanta Kumar Sahoosrikantasahoo@soa.ac.inDepartment of Cyber SecurityInstitute of Technical Education and ResearchSiksha ‘O’ Anusandhan (Deemed to be University)Bhubaneswar, Odisha, 751030, IndiaView full profile → , *Aseem AnejaCorresponding authoraseem.aneja.orp@chitkara.edu.inCentre of Research Impact and Outcome (CRIO)Chitkara UniversityRajpura, Punjab, 140417, IndiaView full profile → , Ramachandran Thulasiramt.ramachandran@jainuniversity.ac.inDepartment of Mechanical EngineeringFaculty of Engineering and TechnologyJAIN (Deemed-to-be University)Ramnagar, Karnataka, 562112, IndiaView full profile → , Nikhil Govilnikhil.govil@gla.ac.inDepartment of Computer Engineering & ApplicationsGLA UniversityMathura, Uttar Pradesh, 281406, IndiaView full profile → , Shoyimkulov Asrorasrorbek.shoimkulov@gmail.comDepartment of Information Technology and Exact SciencesTermez University of Economics and ServiceTermez, UzbekistanView full profile → , Anorgul Ashirovaashirova_anorgul@mamunedu.uzDepartment of General Professional SciencesMamun UniversityKhiva, 220900, Uzbekistan0000-0002-3399-7527View full profile → , P. Jeyanthijeyanthicom@maher.ac.inDepartment of CommerceMeenakshi College of Arts and ScienceMeenakshi Academy of Higher Education & ResearchChennai, Tamil Nadu, 600087, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 27 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIM-2814
- Pages:
- 2995–3007
Abstract
In the field of high dimensional computer vision problems, the feature redundancy, high computation complexity and multidimensional structural loss are responsible for low recognition rates. This study introduces a Tensor Subspace Learning Framework (TSLF) which combines Higher-Order Singular Value Decomposition (HOSVD) and Tucker decomposition of tensors, low rank tensor optimization and Alternating Least Squares (ALS) to efficiently learn multilinear features and reduce the dimensionality. The framework is evaluated using classification accuracy, classification precision, classification recall, F1 score, feature preservation rate, reconstruction error, computational time, and memory usage and compared with the classification based on PCA, vector based SVD and CNN feature extraction. Experimental results reveal that proposed method achieved 97.86% accuracy, 96.91% F1-Score, 98.04% feature preservation and 29.8% less computational time when compared to other methods with the reconstruction error 0.021. The key novelty here is the consistent and mathematically sound framework for robust high dimensional computer vision analysis that is provided by a unified tensor decomposition and optimization that is scalable.
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References
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