Transcript Lecture 1 Overview

Bios 760R, Lecture 1
Overview
Overview of the course
Classification and Clustering
The “curse of dimensionality”
 Reminder of some background knowledge
Tianwei Yu
RSPH Room 334
[email protected]
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Course Outline
Instructor:
Tianwei Yu
Office: GCR Room 334
Email: [email protected]
Office Hours: by appointment.
Teaching Assistant:
Mr. Qingpo Cai
Office Hours: TBA
Course Website:
http://web1.sph.emory.edu/users/tyu8/740/index.htm
Overview
Lecture 1
Overview
Lecture 2
Bayesian decision theory
Lecture 3
Similarity measures, Clustering (1)
Lecture 4
Clustering (2)
Classification
Lecture 5
Density estimation and classification
Clustering
Lecture 6
Linear machine
Dimension reduction
Lecture 7
Support vector machine
Lecture 8
Generalized additive model
Lecture 9
Boosting
Lecture 10
Tree and forest
Lecture 11
Bump hunting; Neural network (1)
Lecture 12
Neural network (2)
Lecture 13
Model generalization
Lecture 14
Dimension reduction (1)
Lecture 15
Dimension reduction (2)
Lecture 16
Some applications
Focus of the course:
The Clustering lectures will
be given by
Dr. Elizabeth Chong.
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Overview
References:
Textbook:
The elements of statistical learning. Hastie, Tibshirani &
Friedman.
Free at: http://statweb.stanford.edu/~tibs/ElemStatLearn/
Other references:
Pattern classification. Duda, Hart & Stork.
Data clustering: theory, algorithms and application. Gan, Ma &
Wu.
Applied multivariate statistical analysis. Johnson & Wichern.
Evaluation:
Three projects (30% each)
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Overview
Supervised learning
”direct data mining”
Classification
Estimation
Prediction
Unsupervised learning
”indirect data mining”
Clustering
Association rules
Description, dimension
reduction and
visualization
Machine Learning
/Data mining
Semi-supervised learning
Modified from Figure 1.1 from <Data Clustering> by Gan, Ma and Wu
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Overview
In supervised learning, the problem is well-defined:
Given a set of observations {xi, yi},
estimate the density Pr(Y, X)
Usually the goal is to find the model/parameters to
minimize a loss,
A common loss is Expected Prediction Error:
It is minimized at
Objective criteria exists to measure the success of a supervised
learning mechanism.
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Overview
In unsupervised learning, there is no output variable, all we
observe is a set {xi}.
The goal is to infer Pr(X) and/or some of its properties.
When the dimension is low, nonparametric density estimation is
possible;
When the dimension is high, may need to find simple properties
without density estimation, or apply strong assumptions to
estimate the density.
There is no objective criteria from the data itself; to justify a
result: > Heuristic arguments,
> External information,
> Evaluate based on properties of the data
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Classification
The general scheme.
An example.
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Classification
In most cases, a single
feature is not enough to
generate a good
classifier.
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Classification
Two extremes:
overly rigid and
overly flexible
classifiers.
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Classification
Goal: an optimal trade-off between model simplicity and training
set performance.
This is similar to the AIC / BIC / …… model selection in
regression.
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Classification
An example of the overall
scheme involving
classification:
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Classification
A classification
project:
a systematic view.
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Clustering
Assign observations into clusters, such that those within each cluster
are more closely related to one another than objects assigned to
different clusters.
 Detect data relations
 Find natural hierarchy
 Ascertain the data consists of distinct subgroups
 …...
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Clustering
Mathematically, we hope to estimate the number of clusters k, and the
membership matrix U
In fuzzy clustering, we have
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Clustering
Some clusters are well-represented by
center+spread model; Some are not.
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Curse of Dimensionality
Bellman R.E.,
1961.
In p-dimensions, to get a hypercube with volume r, the edge length
needed is r1/p.
In 10 dimensions, to capture 1% of the data to get a local average,
we need 63% of the range of each input variable.
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Curse of Dimensionality
In other words,
To get a “dense” sample, if we need N=100 samples in 1
dimension, then we need N=10010 samples in 10 dimensions.
In high-dimension, the data is always sparse and do not support
density estimation.
More data points are closer to the boundary, rather than to any
other data point  prediction is much harder near the edge of the
training sample.
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Curse of
Dimensionality
Estimating a 1D
density with 40
data points.
Standard normal
distribution.
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Curse of
Dimensionality
Estimating a 2D
density with 40
data points.
2D normal
distribution; zero
mean; variance
matrix is identity
matrix.
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Curse of Dimensionality
Another example – the EPE of the nearest neighbor predictor.
To find E(Y|X=x), take the average of data points close to a given x, i.e. the
top k nearest neighbors of x
Assumes f(x) is well-approximated
by a locally constant function
When N is large, the neighborhood
is small, the prediction is accurate.
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Curse of Dimensionality
Just a reminder, the expected prediction error contains
variance and bias components.
Under model:
Y=f(X)+ε
EPE ( x0 ) = E[(Y - fˆ ( x0 )) 2 ]
= E[(e 2 + 2e ( f ( x0 ) - fˆ ( x0 )) + ( f ( x0 ) - fˆ ( x0 )) 2 )]
= s 2 + E[( f ( x ) - fˆ ( x )) 2 ]
0
0
= s 2 + E[ fˆ ( x0 ) - E ( fˆ ( x0 ))]2 + [ E ( fˆ ( x0 )) - f ( x0 )]2
= s 2 + Var ( fˆ ( x )) + Bias 2 ( fˆ ( x ))
0
0
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Curse of Dimensionality
Data:
Uniform in [−1, 1]p
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Curse of Dimensionality
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Curse of Dimensionality
We have talked about the curse of dimensionality in the
sense of density estimation.
In a classification problem, we do not necessarily need
density estimation.
Generative model --- care about the mechanism: class
density function.
Learns p(X, y), and predict using p(y|X).
In high dimensions, this is difficult.
Discriminative model --- care about boundary.
Learns p(y|X) directly, potentially with a subset of X.
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Curse of Dimensionality
X1
Generative model
X2
y
X3
…
Discriminative
model
y
Example: Classifying belt fish and carp. Looking at the
length/width ratio is enough. Why should we care how many
teeth each kind of fish have, or what shape fins they have? 26
Reminder of some results for random vectors
Wiki
The multivariate Gaussian distribution:
The covariance
matrix, not limited to
Gaussian.
* Gaussian is fully
defined by mean
vector and covariance
matrix (first and
second moments).
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Reminder of some results for random vectors
The correlation matrix:
Relationship with covariance matrix:
V
1
1
2
V rV
2
[
= diag s ii
1
2
]
=S
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Reminder of some results for random vectors
k
k
x ¢Ax = å å aij x i x j
Quadratic form:
i=1 j=1
A linear combination of the elements of a random vector with mean μ
and variance-covariance matrix Σ:
2-D example:
Var(aX1 + bX 2 ) = E[(aX1 + bX 2 ) - (am1 + bm2 )]2
= E[a(X1 - m1 ) + b(X 2 - m2 )]2
és11 s12 ùéaù
= a s11 + b s 22 + 2abs12 = [a b]ê
úê ú = c ¢Sc
ës12 s 22 ûëbû
2
2
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Reminder of some results for random vectors
A “new” random vector generated from linear combinations of a
random vector:
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Reminder of some results for random vectors
Let A be a kxk square symmetrix matrix, then it has k pairs of eigenvalues
and eigenvectors. A can be decomposed as:
A = l1e1e1¢ + l2e2e2¢ + ....... + lkekek¢ = PLP ¢
Positive-definite matrix:
x ¢Ax > 0,"x ¹ 0
l1 ³ l2 ³ ...... ³ lk > 0
Note : x ¢Ax = l1 ( x ¢e1 ) 2 + ...... + lk ( x ¢ek ) 2
Reminder of some results for random vectors
Inverse of a positive-definite matrix:
k
A = PL P ¢ = å
-1
-1
i=1
1
li
eiei¢
Square root matrix of a positive-definite matrix:
k
A1 2 = PL 2 P ¢ = å li eiei¢
1
i=1
Reminder of some results for random vectors
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Reminder of some results for random vectors
Proof of the first (and second) point of the previous slide.
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Reminder of some results for random vectors
With a sample of the random vector:
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Reminder of some results for random vectors
To estimate mean vector and covariance matrix:
⌢
m=X
n
⌢
1
¢
S=S=
X
X
X
X
(
)
(
)
å
i
i
n -1 i=1
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Reminder of the ROC curve
J Clin Pathol 2009;62:1-5
Reminder of the ROC curve