Explored SVD for Image Compression and Teaching

• Day: 2024-10-31
• Time: 17:35 to 19:10
• Project: Teaching
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: SVD, Image Compression, Teaching, Numerical Methods, Data Analysis

Description

Session Goal:

The session aimed to explore various numerical methods and their applications, focusing on Singular Value Decomposition (SVD) for image compression and teaching strategies.

Key Activities:

1. Cooking Recipes: Briefly reviewed recipes using harina de mandioca.
2. Polynomial Approximation: Reflected on using normal equations for approximating the sine function.
3. Least-Squares Problem: Planned the use of orthogonal factorizations for solving least-squares problems, emphasizing numerical stability.
4. SVD Image Compression: Developed a structured outline for a notebook on SVD-based image compression.
5. Teaching SVD: Planned an educational approach to teaching SVD and image compression, focusing on theory and practical exercises.
6. SVD and PCA Equivalence: Reflected on the equivalence between SVD and PCA, highlighting efficient data analysis techniques.

Achievements:

• Created a comprehensive outline for an SVD image compression notebook.
• Developed a teaching strategy for SVD in image compression.
• Clarified the equivalence of SVD and PCA for dimensionality reduction.

Pending Tasks:

• Implement the SVD image compression notebook.
• Develop teaching materials based on the planned strategy for SVD.
• Further explore numerical stability in polynomial approximations.

Evidence

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