Unsupervised learning

class: center, middle

# Unsupervised learning

Hugo RICHARD

.affiliations[
ENSAE, CREST
]

.footnote.tiny[Credits: Based on the [slides](https://data-psl.github.io/lectures2021/slides/08_unsupervised_learning/) of Pierre Ablin]

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# Overview:

- Dimension reduction methods
- Matrix factorization

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# Unsupervised learning

We have some data, but no label :(

- Labelling is often time consuming
- Many data are not labelled (e.g. all images in google...)

Goal of unsupervised learning:
Analyse the structure of the dataset

.center[
<img src="images/catsndogs.png" style="width: 450px;" />
 
]

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# Dimension reduction

Data $\mathbf{x}$ lives in dimension $p$

### Notation:
- $\mathbf{x}_i$ is the sample $i$ (vector of size $p$)
- $x_i^a$ is the feature $a$ of $\mathbf{x}_i$ (real number)

**Dimension reduction**: build a transform $\mathbf{z}= f(\mathbf{x})$, where $z$ is in dimension $q < p$, such that most information caried by $\mathbf{x}$ is kept in $\mathbf{z}$

Dimension reduction questions:

- What kind of transform ?
- How to define "information"?

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# Principal component analysis (PCA)

- Linear transform
- Information measured as power

We have $n$ samples $x_1, \dots, x_n$

Assume that the dataset is centered ($\sum \mathbf{x}_i = 0$).

--
To extract $1$ component:
- Look for a projection: $z_i = \mathbf{w}^{\top}\mathbf{x}_i$, where $\mathbf{w}$ is a vector of unit norm
- We want to maximize the power $\sum z_i^2$

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# PCA and correlation

- Look for a projection: $z_i = \mathbf{w}^{\top}\mathbf{x}_i$, where $\mathbf{w}$ is a vector of unit norm
- We want to maximize the power $P = \sum z_i^2$

--
We have:

$$P=\sum (\mathbf{w}^{\top}\mathbf{x}_i)^2 = \sum \mathbf{w}^{\top}\mathbf{x}_i\mathbf{x}_i^{\top}\mathbf{w} =  \mathbf{w}^{\top}\left(\sum \mathbf{x}_i\mathbf{x}_i^{\top}\right)\mathbf{w} =  \mathbf{w}^{\top}C\mathbf{w}$$

where $C$ is the $p\times p$ matrix

$$C = \sum_{i=1}^n \mathbf{x}_i\mathbf{x}_i^{\top}$$

--
$C$ is called the **correlation** matrix

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# Correlation matrix

$$C =  \sum_{i=1}^n \mathbf{x}_i\mathbf{x}_i^{\top}$$

Diagonal coefficients:

--
$C^{aa}= \sum_{i=1}^n (x_i^a)^2$

'Power' of the feature $a$

Off-diagonal coefficients:
$C^{ab}= \sum_{i=1}^n (x_i^a)(x_i^b)$

Correlation between feature $a$ and $b$

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# Correlation matrix

.center[
<img src="images/correlation_00.png" style="width: 450px;" />
 
]

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# Correlation matrix

.center[
<img src="images/correlation_0.png" style="width: 450px;" />
 
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# Correlation matrix

.center[
<img src="images/correlation_10.png" style="width: 450px;" />
 
]

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# Correlation matrix

.center[
<img src="images/correlation_1.png" style="width: 450px;" />
 
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# Correlation matrix

.center[
<img src="images/correlation_20.png" style="width: 450px;" />
 
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# Correlation matrix

.center[
<img src="images/correlation_2.png" style="width: 450px;" />
 
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# Direction with most power

.center[
<img src="images/correlation_pow0.png" style="width: 750px;" />
 
]

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# Direction with most power

.center[
<img src="images/correlation_pow1.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow2.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow3.png" style="width: 750px;" />
 
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# Direction with most power

.center[
<img src="images/correlation_pow4.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow5.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow6.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow7.png" style="width: 750px;" />
 
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# Direction with most power

.center[
<img src="images/correlation_pow8.png" style="width: 750px;" />
 
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# Direction with most power

.center[
<img src="images/correlation_pow9.png" style="width: 750px;" />
 
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# Direction with most power

.center[
<img src="images/correlation_pow10.png" style="width: 750px;" />
 
]

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# Direction with most power

.center[
<img src="images/correlation_pow11.png" style="width: 750px;" />
 
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# Direction with most power

.center[
<img src="images/correlation_pow12.png" style="width: 750px;" />
 
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# Direction with most power

.center[
<img src="images/correlation_pow13.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow14.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow15.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow16.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow17.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow18.png" style="width: 750px;" />
 
]
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# Direction with most power

.center[
<img src="images/correlation_pow19.png" style="width: 750px;" />
 
]

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# PCA and eigenvalue decomposition

The vector $\mathbf{w}$ that maximizes $\mathbf{w}^{\top}C\mathbf{w}$ is the leading eigenvector of $C$.
The corresponding power is the eigenvalue.

First PCA dimension: $z^1 = \mathbf{w}_1^{\top}\mathbf{x}$

Unexplained data:

$$\tilde{\mathbf{x}} = \mathbf{x} - z^1 \mathbf{w}_1$$

- What if $\tilde{\mathbf{x}} = 0$ ?

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# PCA: other components

Unexplained data:

$$\tilde{\mathbf{x}} = \mathbf{x} - z^1 \mathbf{w}_1$$

We can repeat the procedure and find $\mathbf{w}_2$ that maximizes the power of $z^2 = \mathbf{w}_2^{\top}\tilde{\mathbf{x}}$

Corresponds to the second eigenvector of the covariance matrix.

You can iterate as long as you want, giving you $q$ coefficients.

If unexplained data $=0$ with $q$ components, it means that the data is of dim $q$

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# What is the dimension of real datasets?

As we have seen, the covariance matrix is important, its eigenvalues give the correponding powers.

Digits dataset: `from sklearn.datasets import load_digits`

.center[
<img src="images/digits.png" style="width: 450px;" />
 
]

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# What is the dimension of real datasets?

Spectrum of the covariance:

.center[
<img src="images/spectrum.png" style="width: 450px;" />
 
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# 2-D PCA on digits:

.center[
<img src="images/pca_1.png" style="width: 650px;" />
 
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# 2-D PCA on digits:

.center[
<img src="images/pca_2.png" style="width: 650px;" />
 
]

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# Matrix factorization

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# Another formulation of PCA

The data $\mathbf{x}_1,\dots, \mathbf{x}_n$ can be represented as a matrix $X$ of size $p \times n$:
each *row* is a sample.

## **WARNING!!!!**

In `scikit-learn`, the data is represented as $n\times p$

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# PCA as a matrix factorization

The data $\mathbf{x}_1,\dots, \mathbf{x}_n$ can be represented as a matrix $X$ of size $p \times n$:
each *row* is a sample.

- PCA finds a set of weights $\mathbf{w}_1, \cdots, \mathbf{w_q}$, which can be seen as a matrix $W$ of size $p\times q$.
- Transformed data, $z^1, \dots, z^q$ can be seen as a matrix $Z$ of size $q \times n$

We have:

$$X \simeq W Z$$

### Data $X$ is **factorized** as a product of two matrices

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# Matrix factorization: general principle

Factorize $X$ as a product
$$X\simeq WZ$$

- Factorization can be *undercomplete* : $q \leq p$
- *overcomplete*: $q \geq p$

Inherent indeterminacy:

If $(W, Z)$ is a factorization of $X$, for $R$ a matrix of size $q\times q$:

$$(WQ, Q^{-1}Z)$$

is also a factorization (because $WQQ^{-1}Z = WZ$)

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# Usual matrix factorization methods

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# Sparse Dictionary learning

$W$ is called the *dictionary* and $Z$ the *code*

--
### Specificity:
The code is **sparse**:

- Each $\mathbf{z}_i$ only has a few non-zero coefficients
- Each sample $\mathbf{x}_i$ is represented as a linear combination of a **few** elements of the dictionary

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# Sparse dictionary learning: example

On image dataset, learned dictionary:

.center[
<img src="images/gabor.png" style="width: 650px;" />
 
]

### Gabor filters

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# Non-negative matrix factorization

### Specificity

- Works only when the data features are all positive
- $W$ and $Z$ contain only positive values

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# Non-negative matrix factorization: applications

$X$: spectrogram of a song. $p$ = frequency, $n$ = times
.center[
<img src="images/spect.png" style="width: 600px;" />
 
]

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# Non-negative matrix factorization: applications

- $W$ contains the spectral signatures

.center[
<img src="images/dict.png" style="width: 600px;" />
 
]

- $Z$ contains temporal activations

.center[
<img src="images/activ.png" style="width: 600px;" />
 
]

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# Non-negative matrix factorization: applications

.center[
<img src="images/dict.png" style="width: 500px;" />
 
]

.center[
<img src="images/activ.png" style="width: 500px;" />
 
]

### Corresponds to this signal

.center[
<img src="images/notes.png" style="width: 500px;" />
 
]

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# Non-negative matrix factorization: applications

- Each element in $W$ corresponds to a distinct frequency signature:

### NMF can separate the musical instruments from each other (drums, piano, etc...)

This is completely unsupervised !

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# Independent component analysis

### Specificity

- $W$ is square ($p=q$)
- The rows of $Z$ are statistically **independent**

PCA gives decorrelated rows in $Z$. ICA goes further by imposing independence

## Independence => decorrelation

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# Power of independence:

$$X$$

.center[
<img src="images/data.png" style="width: 600px;" />
 
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# Power of independence:

$$Z_{PCA}$$

.center[
<img src="images/pca_data.png" style="width: 600px;" />
 
]
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# Power of independence:

$$Z_{ICA}$$

.center[
<img src="images/ica_data.png" style="width: 600px;" />
 
]

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### Electrocardiogram of a pregnant mother

.center[
<img src="images/ecg.png" style="width: 400px;" />
 
]

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### ICA decomposition

.center[
<img src="images/ecg_ica.png" style="width: 400px;" />
 
]