Lecture7a.slides.pdf

# Lecture7a.slides.pdf - CS4487 Machine Learning Lecture 7...

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CS4487 - Machine Learning Lecture 7 - Linear Dimensionality Reduction Dr. Antoni B. Chan Dept. of Computer Science, City University of Hong Kong Outline 1. Linear Dimensionality Reduction for Vectors A. Principal Component Analysis (PCA) B. Random Projections C. Fisher's Linear Discriminant (FLD) 2. Linear Dimensionality Reduction for Text A. Latent Semantic Analysis (LSA) B. Non-negative Matrix Factorization (NMF) C. Latent Dirichlet Allocation (LDA) Dimensionality Reduction Goal: Transform high-dimensional vectors into low-dimensional vectors. Dimensions in the low-dim data represent co-occuring features in high-dim data. Dimensions in the low-dim data may have semantic meaning. For example: document analysis high-dim: bag-of-word vectors of documents low-dim: each dimension represents similarity to a topic.

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Example: image analysis approximate an image as a weighted combination of several basis images represent the image as the weights. Reasons for Dimensionality Reduction
Preprocessing - make the dataset easier to use Reduce computational cost of running machine learning algorithms Remove noise Make the results easier to understand (visualization) Linear Dimensionality Reduction Project the original data onto a lower-dimensional hyperplane (e.g., line, plane). I.e, Move and rotate the coordinate axis of the data Represent the data with coordinates in the new component space. Equivalently, approximate the data point as a linear combination of basis vectors (components) in the original space. original data point approximation: is a basis vector and the corresponding weight. the data point is then represented its corresponding weights Several methods for linear dimensionality reduction. Di ff erences: goal (reconstruction vs classification) unsupervised vs. supervised constraints on the basis vectors and the weights. reconstruction error criteria Principal Component Analysis (PCA) Unsupervised method Goal: preserve the variance of the data as much as possible choose basis vectors along the maximum variance (longest extent) of the data. the basis vectors are called principal components (PC). x x ∈ ℝ d = x ̂ p j =1 w j v j v j d ∈ ℝ w j x w = [ , , ] w 1 w P p

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In [4]: vfig Goal: Equivalently, minimize the reconstruction error over all the data points . reconstruction: constraint: principal components are orthogonal (perpendicular) to each other. PCA algorithm 1) subtract the mean of the data 2) the first PC is the direction that explains the most variance of the data.
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