Introduction to Pattern Recognition and Machine Learning. Alexandros Iosifidis Academy of Finland Postdoctoral Research Fellow (term )
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1 Introduction to Pattern Recognition and Machine Learning Alexandros Iosifidis Academy of Finland Postdoctoral Research Fellow (term ) Tampere, August 2016
2 What do we observe here? Vector Spaces
3 Vector Spaces What do we observe here? A set of N points in the 2-dimensional space
4 Vector Spaces What do we observe here? A set of N points in the 2-dimensional space Each point can be represented by a vector x i ={x i1,x i2 }, i=1,n x 2 x 1
5 Vector Spaces What do we observe here? A set of N points in the 2-dimensional space Each point can be represented by a vector x i ={x i1,x i2 }, i=1,n We call each point as sample and we say that x i is the representation of the i-th sample
6 Vector Spaces What do we observe here? A set of N points in the 2-dimensional space Each point can be represented by a vector x i ={x i1,x i2 }, i=1,n We call each point as sample and we say that x i is the representation of the i-th sample We say that x i belongs to the 2D vector space or or, D=2.
7 Vector Spaces What is a sample representation x i? Data representation is followed by a description, e.g. we can describe a person by using his/hers height and weight. In that case: Alekos {187, 110} x 2 x i2 x i x i1 x 1
8 Vector Spaces What is a sample representation x i? Another representation could be followed by the description of {height, weight, gender}. In that case: Alekos {187, 110, 1} Real value
9 Vector Spaces What is a sample representation x i? Another representation could be followed by the description of {height, weight, gender}. In that case: Alekos {187, 110, 1} Let us assume that all vectors represent males. Does it make sense to keep the last dimension?
10 Vector Spaces What is a sample representation x i? Another representation could be followed by the description of {height, weight, gender}. In that case: Alekos {187, 110, 1} Let us assume that all vectors represent males. Does it make sense to keep the last dimension? Sometimes data dimensions contain low information. Sometimes data dimensions can be combined in order to enhance properties of interest.
11 Vector Spaces What is a sample representation x i? Another representation could be followed by the description of {height, weight, gender}. In that case: Alekos {187, 110, 1} Let us assume that all vectors represent males. Does it make sense to keep the last dimension? Sometimes data dimensions contain low information. Sometimes data dimensions can be combined in order to enhance properties of interest. Subspace learning: Define a vector space, d < D, in which the data representations have some properties of interest.
12 Another example: Vector Spaces
13 Vector Spaces Input space R D R d, d D where data dispersion is maximized
14 Vector Spaces The same example: Each sample is followed by a class label (red or green)
15 Vector Spaces Input space R D R d, d D, where the dispersion within the classes is minimized and between the classes is maximized
16 Basic Problems in PR and ML Pattern Recognition problems can be divided in: Unsupervised: the only available information is the data representations, i=1,,n
17 Basic Problems in PR and ML Pattern Recognition problems can be divided in: Unsupervised: the only available information is the data representations, i=1,,n Supervised: data representations, i=1,,n and class labels l i, i=1,,n, l i Є {1,,C} are available
18 Basic Problems in PR and ML Pattern Recognition problems can be divided in: Unsupervised: the only available information is the data representations, i=1,,n Supervised: data representations, i=1,,n and class labels l i, i=1,,n, l i Є {1,,C} are available Semi-supervised: a (small) sub-set of the data are labeled, i.e.:, i=1,,n and l i, i=1,,n<n, l i Є {1,,C} are available
19 Basic Problems in PR and ML Data clustering: Define a set of data groups (clusters) according to a (data closeness/similarity) criterion Unsupervised problem
20 Basic Problems in PR and ML Data clustering: Define a set of data groups (clusters) according to a (data closeness/similarity) criterion Unsupervised problem
21 Basic Problems in PR and ML Data clustering: Define a set of data groups (clusters) according to a (data closeness/similarity) criterion Unsupervised problem Is this the correct answer?
22 Basic Problems in PR and ML Data clustering: Define a set of data groups (clusters) according to a (data closeness/similarity) criterion Unsupervised problem Is this the correct answer?
23 Basic Problems in PR and ML Data classification: Samples belonging to two patterns (classes) Can we define a rule for recognizing if a new sample (the yellow one) belongs to the red or green pattern?
24 Basic Problems in PR and ML Data classification: Samples belonging to two patterns (classes) Can we define a rule for recognizing if a new sample (the yellow one) belongs to the red or green pattern? - +
25 Basic Problems in PR and ML Data classification: Samples belonging to two patterns (classes) Can we define a rule for recognizing if a new sample (the yellow one) belongs to the red or green pattern? - +
26 Basic Problems in PR and ML Data classification: Samples belonging to two patterns (classes) Can we define a rule for recognizing if a new sample (the yellow one) belongs to the red or green pattern? Which one?
27 Basic Problems in PR and ML Data classification: Some of the samples are followed by labels Can we define a rule for recognizing if a new sample (the yellow one) belongs to the red or green pattern?
28 Basic Problems in PR and ML Data classification (semi-supervised): Some of the samples are followed by labels Can we define a rule for recognizing if a new sample (the yellow one) belongs to the red or green pattern? - +
29 Basic Problems in PR and ML Data classification (semi-supervised): Some of the samples are followed by labels Can we define a rule for recognizing if a new sample (the yellow one) belongs to the red or green pattern? + - Many possible solutions!
30 Nonlinearity Multi-class classification: Split the problem in multiple binary (two-class) problems: One-Versus-One and One-Versus-Rest
31 Nonlinearity Multi-class classification: One-Versus-Rest case
32 Nonlinearity Multi-class classification: One-Versus-Rest case
33 Nonlinearity Multi-class classification: One-Versus-Rest case Sometimes linear models are not the best solution
34 Nonlinearity Multi-class classification: A better solution!
35 Nonlinearity Nonlinear approaches: Piecewise linear methods: approximate the nonlinear functions with multiple linear ones
36 Nonlinearity Nonlinear approaches: Piecewise linear methods
37 Nonlinearity Nonlinear approaches: Piecewise linear methods Kernel-based methods: Map the data (usually nonlinearly) to a feature space where the problem is easier to be solved
38 Nonlinearity Nonlinear approaches: Piecewise linear methods Kernel-based methods Input space R D Feature space Input space R D
39 Nonlinearity Nonlinear approaches: Piecewise linear methods Kernel-based methods Neural Networks - NN with one hidden layer - Samples x i are (nonlinearly) mapped to a feature space - The use of multiple hidden layer is also possible (each transforming the data representations in the previous layer to a new feature space). x i C t i
40 Coming next! More on PR and ML stuff during this training: Thursday :00-16:00: Evolutionary Learning (Prof. Serkan Kiranyaz) Friday :00-12:00: Probabilistic and Statistical Learning (Myself) 13:00-16:00: Neural Networks (Prof. Anastasios Tefas) If you want to find out more:
41 Questions?
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