dimensionality reduction motivation i: data compression machine learning
TRANSCRIPT
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Dimensionality ReductionMotivation I: Data Compression
Machine Learning
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Data Compression(in
ches
)
(cm)
Reduce data from 2D to 1D
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Data Compression
Reduce data from 2D to 1D
(inch
es)
(cm)
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Data CompressionReduce data from 3D to 2D
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Dimensionality ReductionMotivation II: Data Visualization
Machine Learning
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Data Visualization
[resources from en.wikipedia.org]
Country
GDP (trillions of
US$)
Per capita GDP
(thousands of intl. $)
Human Develop-
ment IndexLife
expectancy
Poverty Index
(Gini as percentage)
Mean household
income (thousands
of US$) …Canada 1.577 39.17 0.908 80.7 32.6 67.293 …China 5.878 7.54 0.687 73 46.9 10.22 …India 1.632 3.41 0.547 64.7 36.8 0.735 …
Russia 1.48 19.84 0.755 65.5 39.9 0.72 …Singapore 0.223 56.69 0.866 80 42.5 67.1 …
USA 14.527 46.86 0.91 78.3 40.8 84.3 …… … … … … … …
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Data Visualization
CountryCanada 1.6 1.2
China 1.7 0.3India 1.6 0.2
Russia 1.4 0.5Singapore 0.5 1.7
USA 2 1.5… … …
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Data Visualization
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Dimensionality ReductionPrincipal Component Analysis problem formulation
Machine Learning
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Principal Component Analysis (PCA) problem formulation
The red line is the projections error
PCA tries to minimize the projections error
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Principal Component Analysis (PCA) problem formulation
The red line is the projections error
Example for large projection error
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Principal Component Analysis (PCA) problem formulation
Reduce from 2-dimension to 1-dimension: Find a direction (a vector )onto which to project the data so as to minimize the projection error.Reduce from n-dimension to k-dimension: Find vectors onto which to project the data, so as to minimize the projection error.
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PCA is not linear regression
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PCA is not linear regression
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Dimensionality ReductionPrincipal Component Analysis algorithmMachine Learning
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Training set:Preprocessing (feature scaling/mean normalization):
Data preprocessing
Replace each with .If different features on different scales (e.g., size of house, number of bedrooms), scale features to have comparable range of values.
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Principal Component Analysis (PCA) algorithm
Reduce data from 2D to 1D Reduce data from 3D to 2D
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Principal Component Analysis (PCA) algorithmReduce data from -dimensions to -dimensionsCompute “covariance matrix”:
Compute “eigenvectors” of matrix :[U,S,V] = svd(Sigma);
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Covariance Matrix• Covariance measures the degree to which two variables change or vary
together (i.e. co-vary). • On the one hand, the covariance of two variables is positive if they vary
together in the same direction relative to their expected values (i.e. if one variable moves above its expected value, then the other variable also moves above its expected value).
• On the other hand, if one variable tends to be above its expected value when the other is below its expected value, then the covariance between the two variables is negative.
• If there is no linear dependency between the two variables, then the covariance is 0.
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Principal Component Analysis (PCA) algorithmFrom , we get: [U,S,V] = svd(Sigma)
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Principal Component Analysis (PCA) algorithm summary
After mean normalization (ensure every feature has zero mean) and optionally feature scaling:
Sigma =
[U,S,V] = svd(Sigma);Ureduce = U(:,1:k);z = Ureduce’*x;
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Dimensionality ReductionReconstruction from compressed representation
Machine Learning
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Reconstruction from compressed representation
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Dimensionality ReductionChoosing the number of principal components
Machine Learning
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Choosing (number of principal components)Average squared projection error:Total variation in the data:
Typically, choose to be smallest value so that
“99% of variance is retained”
(1%)
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Choosing (number of principal components)Algorithm:Try PCA with Compute
Check if
[U,S,V] = svd(Sigma)
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Choosing (number of principal components)[U,S,V] = svd(Sigma)
Pick smallest value of for which
(99% of variance retained)
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Dimensionality Reduction
Advice for applying PCA
Machine Learning
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Supervised learning speedup
Extract inputs:Unlabeled dataset:
New training set:
Note: Mapping should be defined by running PCA only on the training set. This mapping can be applied as well to the examples and in the cross validation and test sets.
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Application of PCA
- Compression- Reduce memory/disk needed to store data- Speed up learning algorithm
- Visualization
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Bad use of PCA: To prevent overfittingUse instead of to reduce the number of features to Thus, fewer features, less likely to overfit.
This might work OK, but isn’t a good way to address overfitting. Use regularization instead.
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PCA is sometimes used where it shouldn’t beDesign of ML system:
- Get training set- Run PCA to reduce in dimension to get- Train logistic regression on- Test on test set: Map to . Run on
How about doing the whole thing without using PCA?Before implementing PCA, first try running whatever you want to do with the original/raw data . Only if that doesn’t do what you want, then implement PCA and consider using .