# Introduction to Dimensionality Reduction and Linear Algebra basics (part 2)

**Author : Matteo Alberti**

** **

Sommario

Dimensionality Reduction in a linear space. 1

Through the identification of a subspace. 1

Reduction through matrix approximations. 1

Basic case: Decomposition in Singular Values (SVD). 1

**Base Cases: Cluster Analysis. 1**

**Minkowski Distance (Manhattan, Euclidean, Lagrange). **

# Matricial Norms

At this point we have set the problem of dimensionality reduction of data as an approximation problem between matrices, we must now evaluate and then calculate the distance between the matrix of the original and the approximate data through the study of the different standards:

There are three main types of rules:

- Vector norms
- Induced norms
- Schatten norms

Where we essentially refer, exceptions excluded, to the Frobenius norm (Euclidean distance)

*Elements of algebra:*

*Norm*

A standard (commonly marked with ‖ ∙ ‖) is a function from the vector space fo matrix if:

## Vectorial Norms

The vector norms family treats the array as a vector of nk components where we can define the norm using any of the following rules:

Note:

Setting p = 2 we are connected to the Euclidean norm

## Induced Norms

An matrix can be seen as a linear operator from .

Measuring in the lengths with a fixed norm and doing the same in , with a different norm, we can go to measure how much X lengthens or shortens a vector , comparing the norm of v with the relative norm of his image Xv.

The induced norm is defined as:

## Schatten Norms

The Schatten norm, of order p, of an X matrix, is simply given by:

Where are singular values

## Frobenius Norms

The Frobenius norm of our matrix is given by:

Explicating the matrix product we obtain:

It corresponds that the Frobenius norm is equal to the sum of square roots of the square.

A Euclidean norm is seen as a vector coincides with the vector rule of X of order 2.

*Elements of algebra:*

*Trace*

The trace operator, indicated by Tr (∙), is defined as the sum of the diagonal elements of the argument matrix

# Base Cases: Cluster Analysis

Cluster analysis is a multivariate analysis technique through which it is possible to group statistical units, to minimize the internal “logical distance” to each group and to maximize the one between the groups.

It is among the unsupervised learning techniques.

It is therefore spontaneous to have to define what is meant by logical distance and based on which metric.

## Definition of a Metrics

If it contrarily enjoys only the first three properties, we can define it as an index of the distance

## Minkowski Distance (Manhattan, Euclidean, Lagrange)

At this point we are going to analyze the main cases of distances belonging to the Minkowski distances family where:

We highlight the following cases:

We highlight the following cases:

- Manhattan distance
- Euclidean distance
- Lagrangian distance (Čebyšëv)

As we can see:

Therefore, starting with the example of Cluster Analysis, it is essential to define the type of distance with which we want to deal with our analysis.

Mainly in the packages already implemented are the three variants of Minkowski distances (for quantitative variables)

Importing from sklearn:

*AgglomerativeClustering(n_clusters=2, affinity=’euclidean’, memory=None, connectivity=None, compute_full_tree=’auto’, linkage=’ward’*

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