Principle Component Analysis

講者：羅右鈞

日期：2016.10.27

地點：清華大學

Intuition

Let's say, we've collected some data with the following 4 attributes,

and aim to build model to perform some prediction tasks...

Intuition

In fact ... all these attributes are same as "height" in different languages and be converted to different units

• "tingg" = "dhuwur" = "altuera" = "teitei" = "height"
• In other words, all attributes are dependent on some quantity which aren't easy to be observed
• We can describe data by only 1 attribute!

Curse of dimensionality

As dimensionality grows: fewer observations per region.

(sparse data)

1-D: 3 regions

2-D: 9 regions

3-D: 27 regions

1000-D: GG

That's why dimension reduction is important

Dimension Reduction

Goal: represent instances with fewer variables

• try to preserve as much structure in the data as possible
• we want that structure is useful for specific tasks, e.g., regression, classification.

Feature selection

• pick a subset of the original dimensions X1, X2, X3, ..., Xd-1, Xd
• pick good class "predictors", e.g. information gain

Feature extraction

• Construct a new set of dimensions Ei = f(X1,X2...Xd)
• (linear) combinations of original dimensions
• PCA performs linearly combinations of orignal dimensions

PCA Ideas

Define a set of principle components

• 1st: direction of the greatest variance in data
• 2nd: perpendicular to 1st, greatest variance of what's left

First m << d components become m new dimensions

• You can then project your data with these new dimensions

Why greatest variance?

e preserves more structure than e

• distances between data points in original space remains in new space

How to compute Principle Components ?

1. Center the data at zero (subtracted by mean)
2. Compute covariance matrix Σ

this computes sample variance (N-1 to estimate unbiased variance)

• Normalize(standardize) covariance matrix [-inf, inf] to [-1, 1], we get Pearson correlation coefficient (correlation matrix)
• The correlation coefficient between two random variables x and y are defined as:

if computing population variance, you can just divide by N

How to compute Principle Components (cont.)?

Interesting observation:

• multiply a vector by covariance matrix

x1

x2

e1

e2

slope: 0.400    0.450     0.454...      0.454...

Want vectors e which aren't rotated: Σe = λe

• principle components = eigenvectors with largest eigenvalues

In fact ... (see Read more for proof)

• eigenvector = direction of greatest variance
• eigenvalue = variance along eigenvector

(-1.0, +1.0)

(-1.2, -0.2)

(-14.1 -6.4)

(-33.3 -15.1)

(-6.0, -2.7)

Finally, project original data points to new dimesions by dot product

Read more

• Principle Component Analysis (Main reference)
• A geometric interpretation of the covariance matrix
• Khanacademy: Introduction to eigenvalues and eigenvectors
• Quara: Why does repeatedly multiplying a vector by a square matrix cause the vector to converge on or along the matrix's eigenvector?
• Quara: What do eigenvalues and eigenvectors represent intuitively?

L1 sentence

Encoder

L1 encoded vector

L2 Decoder

L2

sentence

Encoder

L2

encoded vector

L1 Decoder

Inference

Training