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Independent Component Analysis
Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples.
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Which of the following are use-cases of supervised learning?
Principal Components Analysis (PCA)
The Challenge of Unsupervised Learning
Types of unsupervised learning problems
Which ones are true about Supervised statistical learning?
Association
T-Distributed Stochastic Neighbour Embedding (T-SNE)
Clustering, an unsupervised statistical learning method
Advantages of Unsupervised Learning
Collaborative Filtering
Independent Component Analysis
Real-World Applications Of Unsupervised Learning
Slow Feature Analysis
Linear Factor Models Lecture - Berkeley
Slow feature analysis