Interested to Buy Any Domain ? << Click Here >> for more details...
Name some feature extraction techniques used for dimensionality reduction?
- 1 Answers
- 93 Views
- I also Faced
- E-Mail Answers
Answer / Vaibhav Tyagi
Principal Component Analysis (PCA) is a popular technique that transforms the data into a new coordinate system, retaining most of the variance in fewer dimensions. Another method is t-distributed Stochastic Neighbor Embedding (t-SNE), which attempts to preserve local and global structure of high dimensional data while reducing its complexity. Other techniques include Independent Component Analysis (ICA) and Local Linear Embedding (LLE).
| Is This Answer Correct ? | 0 Yes | 0 No |
Is python better than r?
Why is manhattan distance not used in knn machine learning algorithm to calculate the distance between nearest neighbors?
What's the f1 score? How would you use it?
What is Overfitting? And how do you ensure you’re not overfitting with a model?
What is sequential data in machine learning?
Define what is Fourier Transform in a single sentence?
What is the difference between artificial learning and machine learning?
Explain me machine learning in to a layperson?
Why is machine learning important?
What is connectionist AI?
What is A/B testing in Machine Learning?
What is your opinion on our current data process?
-
AI Algorithms (74) - AI Natural Language Processing (96)
- AI Knowledge Representation Reasoning (12)
- AI Robotics (183)
- AI Computer Vision (13)
- AI Neural Networks (66)
- AI Fuzzy Logic (31)
- AI Games (8)
- AI Languages (141)
- AI Tools (11)
- AI Machine Learning (659)
- Data Science (671)
- Data Mining (120)
- AI Deep Learning (111)
- Generative AI (153)
- AI Frameworks Libraries (197)
- AI Ethics Safety (100)
- AI Applications (427)
- AI General (197)
- AI AllOther (6)
