1. Initial Interests
1.1. Dimentionality Reduction
1.1.1. Techniques of DR
1.1.1.1. Linear and Non -linear
1.1.1.2. Supervised and Unsupervised
1.2. Mathematical Modelling
1.2.1. Eigen Value Eigen Vector
2. Literature Review
2.1. Methods
2.1.1. Median LDA
2.1.2. PCA and LDA
2.1.3. R1-PCA
2.2. Research Trends
2.2.1. Comparative Analysis
2.2.1.1. PCA/LDA on Different Datasets
2.2.1.2. PCA and LDA
2.2.1.2.1. Techniques
2.2.2. New Datasets
2.2.2.1. Image Data
2.2.2.2. Geneomic Data
2.2.2.3. Network Traffic Data
2.2.2.3.1. IDS
2.2.2.3.2. Anamoly Detection system
2.2.2.3.3. Network Traffic prediction
3. Problem Refinement
3.1. Dataset
3.1.1. Real-World Dataset
3.1.1.1. Robust
3.1.1.2. noise
3.1.1.3. diverse attacks
3.2. technique
3.2.1. Anomaly Detection
3.3. Method
3.4. Implication
3.4.1. Able to detect different attacks
3.4.2. practical application
3.4.3. robust model
4. Stakeholder Needs
4.1. Practical Implication
4.1.1. Use of Real World Dataset
4.2. Industry Demands
4.2.1. Cybersecurity
4.2.1.1. Advancement in Machine learning
4.2.1.1.1. More efficient model requirement
4.2.1.2. Increase in Cyber attacks