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Introduction:

Tensor factorization is a powerful technique for analyzing high-dimensional data sets. By decomposing multi-dimensional arrays (tensors) into a set of lower-dimensional factors, researchers can uncover patterns, correlations, and structures that may be hidden in the data. This technique has applications in a wide range of fields, including image and video processing, recommendation systems, bioinformatics, and neuroscience.

Table of Contents:

Chapter 1: Introduction
– Introduction
– Objective of Study
– Limitation of Study
– Scope of Study

Chapter 2: Literature Review
– Overview of Tensor Factorization
– Applications of Tensor Factorization
– Comparison with Other Data Analysis Techniques

Chapter 3: Research Methodology
– Data Collection
– Preprocessing
– Tensor Factorization Algorithms
– Evaluation Metrics

Chapter 4: Discussion of Findings
– Results of Tensor Factorization on High-Dimensional Data
– Interpretation of Patterns and Structures
– Comparison with Existing Studies

Chapter 5: Conclusion and Summary
– Summary of Findings
– Contributions to the Field
– Future Directions for Research

Thesis Overview:

Tensor factorization is a cutting-edge technique for analyzing high-dimensional data sets, offering a powerful tool for uncovering hidden patterns and structures. This thesis explores the use of tensor factorization in high-dimensional data analysis, focusing on its applications, limitations, and methodologies. By conducting a comprehensive literature review, discussing research findings, and presenting a detailed methodology, this study aims to provide valuable insights into the effectiveness of tensor factorization in analyzing complex data sets. The thesis concludes with a summary of findings, contributions to the field, and future research directions in the realm of tensor factorization for high-dimensional data analysis.

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