Day 11 Dimensionality Reduction

---

Day 11 Poster

Today I explored Dimensionality Reduction — a vital step to make sense of high-dimensional data 📉🔍

---

🔍 Analogy

Imagine packing for a trip. You have a massive wardrobe (your data), but your suitcase only fits 10 items. So, you pick versatile clothes — a few that cover most needs (formal, casual, warm, cold).

That’s Dimensionality Reduction: Compressing a huge dataset into its most meaningful parts — minimizing loss while maximizing utility.

---

🔹 Two Common Techniques

✅ PCA (Principal Component Analysis)

🧳 Like folding and layering smartly to save space, PCA finds directions (components) that capture the most variance (info).

• Linear method

• Great when data lies in neat, straight patterns

✅ t-SNE (t-distributed Stochastic Neighbor Embedding)

🧳 Like grouping clothes by outfits (shoes + formalwear), t-SNE clusters related data together.

• Non-linear

• Captures local relationships, distorts global structure

• Ideal for visualizing complex, high-dimensional datasets

---

🚀 These techniques help models:

• ✅ Train faster

• ✅ Generalize better

• ✅ Reveal hidden patterns

---

🔐 Security Lens

⚠️ Information Loss & Blind Spots

🎒 You packed for summer, but forgot a raincoat. Rare threats (low variance) may get discarded — making your model blind to anomalies or attacks.

"Low variance" ≠ "low importance" — especially in security contexts.

⚠️ Feature Obfuscation by Attackers

🎒 Attackers can embed malicious patterns in dimensions likely to be discarded or compressed — bypassing detection pipelines.

⚠️ Inference Attacks on Embeddings

🎒 It's like sharing a blurred photo of your bag — someone could still guess your travel habits from the outline of items.

t-SNE visualizations can leak structural info — attackers might infer relationships between users, labels, or features. These compressed representations, if exposed, can be mined or reversed to extract sensitive patterns or identities.

---

📚 Key References

• Jolliffe (2002) – Principal Component Analysis

• Carlini et al. (2020) – Extracting Training Data from Embeddings

• StatQuest: t-SNE, Clearly Explained

---

💬 Prompt

Have you visualized your model with t-SNE? What insights — or vulnerabilities — did you discover?

---

📅 Tomorrow

We explore KNN & Clustering — the simplest ML algorithms and how attackers exploit proximity logic 🔗👥

---

🔗 Missed Day 10?

Catch up here: https://lnkd.in/gMh3rr8b

---

#100DaysOfAISec - Day 11 Post #AISecurity #MLSecurity #MachineLearningSecurity #DimensionalityReduction #CyberSecurity #AIPrivacy #AdversarialML #LearningInPublic #100DaysChallenge #ArifLearnsAI #LinkedInTech