pattern recognition fatoş tunay yarman vural

PATTERN RECOGNITIONFatoş Tunay Yarman Vural

Textbook: Pattern Recognition and Machine Learning, C. Bishop

Reccomended Book: Pattern Theory, U. Granander, M. Miller

Course Requirement

Final:50%Project: 50%Literature Survey report: 1 AprilAlgorithm Development:1 MayFull Paper with implementation 1 June

Content

1.What is Pattern2. Probability Theory3. Bayesian Paradigm4. Information Theory5. Linear Methods6.Kernel Methods7. Graph Methods

WHAT İS PATTERN

• Structures regulated by rules• Goal:Represent empirical knowledge in

mathematical forms• the Mathematics of Perception• Need: Algebra, probability theory, graph

theory

What you perceive is not what you hear:

ACTUAL SOUND1. The ?eel is on the shoe2. The ?eel is on the car3. The ?eel is on the table4. The ?eel is on the orange

PERCEIVED WORDS1. The heel is on the shoe2. The wheel is on the car3. The meal is on the table4. The peel is on the orange

(Warren & Warren, 1970)

Statistical inference is being used!

All flows! Heraclitos

• It is only the invariance, the permanent facts, that enable us to find the meaning in a world of flux.

• We can only perceive variances• Our aim is to find the invariant laws of our

varying obserbvations

Pattern Recognition

ASSUMPTİON

SOURCE:HypothesisClassesObljects

CHANNEL:Noisy

OBSERVATION:Multiple sensorVariations

Example

Handwritten Digit Recognition

Polynomial Curve Fitting

Sum-of-Squares Error Function

0th Order Polynomial

1st Order Polynomial

3rd Order Polynomial

9th Order Polynomial

Over-fitting

Root-Mean-Square (RMS) Error:

Polynomial Coefficients

Data Set Size:

9th Order Polynomial

Data Set Size:

9th Order Polynomial

Regularization

Penalize large coefficient values

Regularization:

Regularization:

Regularization: vs.

Polynomial Coefficients

Probability Theory

Apples and Oranges

Probability Theory

Marginal Probability

Conditional ProbabilityJoint Probability

Probability Theory

Sum Rule

Product Rule

The Rules of Probability

Sum Rule

Product Rule

Bayes’ Theorem

posterior likelihood × prior

Probability Densities

Transformed Densities

Expectations

Conditional Expectation(discrete)

Approximate Expectation(discrete and continuous)

Variances and Covariances

The Gaussian Distribution

Gaussian Mean and Variance

The Multivariate Gaussian

Gaussian Parameter Estimation

Likelihood function

Maximum (Log) Likelihood

Properties of and

Curve Fitting Re-visited

Maximum Likelihood

Determine by minimizing sum-of-squares error, .

Predictive Distribution

MAP: A Step towards Bayes

Determine by minimizing regularized sum-of-squares error, .

Bayesian Curve Fitting

Bayesian Predictive Distribution

Model Selection

Cross-Validation

Curse of Dimensionality

Curse of Dimensionality

Polynomial curve fitting, M = 3

Gaussian Densities in higher dimensions

Decision Theory

Inference stepDetermine either or .

Decision stepFor given x, determine optimal t.

Minimum Misclassification Rate

Minimum Expected Loss

Example: classify medical images as ‘cancer’ or ‘normal’

DecisionTr

uth

Minimum Expected Loss

Regions are chosen to minimize

Reject Option

Why Separate Inference and Decision?

• Minimizing risk (loss matrix may change over time)• Reject option• Unbalanced class priors• Combining models

Decision Theory for Regression

Inference stepDetermine .

Decision stepFor given x, make optimal prediction, y(x), for t.

Loss function:

The Squared Loss Function

y(x), obtained by minimizing the expected squared loss, with respect tonmean of the conditional distribution p(t|x).

Minimum Expected loss= optimal least squares predictor w.r. To the conditional mean +the variance of the distribution of t, averaged over x.

Second term: can be regarded as noise. Because it is independent of y(x),it represents the irreducible minimum value of the loss function.

Minkowsky Metric: Lq = |y - t|q

Generative vs Discriminative

Generative approach: ModelUse Bayes’ theorem

Discriminative approach: Model directly

Information Theory:Claude Shannon 1916-2001

Goal: Find the amount of information carried by a specific value of an r.v.Need something intuitive

Information Theory: C. Shannon

Information: giving form or shape to the mindAssumptions: Source Receiver• Information is the quality of a message • it may be a truth or a lie, • if the amount of information in the received

message increases, the message is more accurate.• Need a common alphabet to communucate

message

Quantification of information.

Given r.v. X and p(x) , what is the amount of information when we receive

an outcome of x? Self Information h(x)= -log p (x)Low probability High info SurpriseBase e: natsBase 2: bits

Entropy: Expected value of self information information needed to specify the state of a random variable.

Why does H(X) measures information?•İt makes sense intuitively•“Nobody knows what entropy really is, so in any discussion you will always have an advan tage". Von Neumann

Entropy

Noiseless Coding theory: • Entropy is a lower bound on the number of bits needed to

transmit the state of random variable.

Ex: discrete with 8 possible states (alphabet); how many bits (only two values using) to transmit the state of x?

All states equally likely,

Entropy

Entropy and Multiplicity

In how many ways can N identical objects be allocated in M bins?

Entropy maximized when

Entropy

Differential Entropy

Put bins of width ¢ along the real line

Differential entropy maximized (for fixed ) when

in which case

Conditional Entropy

The Kullback-Leibler DivergenceAverage additional amount of information required to specify the value of x as a result of using q(x) instead of the true distribution p(x)

Mutual Information