Dimensionality Reduction will be covered in this article, including the components and methods of Dimensionality Reduction, Principle Component Analysis and the Dimensionality Reduction Importance, Feature Selection, and the Advantages and Disadvantages of Dimensionality Reduction. So, let’s get started with the Dimensionality Reduction Introduction.

## 1. What Is Dimensionality Reduction?

We have far too many parameters on which to base the final classification in machine learning. These parameters are referred to as variables. The more characteristics there are, the more difficult it is to visualize the training set and then work on it. Occasionally, the majority of these characteristics are correlated and hence redundant. Dimensionality reduction techniques come into play here.

- When dealing with real-world issues and data, we frequently deal with high-dimensional data that might number in the millions.
- Data reflects itself in the original high-dimensional form. However, there are instances when we need to minimize its dimensionality.
- We need to reduce the dimensionality that needs to associate with visualizations. Although, that is not always the case.

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## 2. Components of Dimensionality Reduction

Dimensionality reduction has two components:

**a. Feature selection**

We must identify a subset of the original collection of variables in this case. We also require a subset to model the problem. It is generally accomplished in three ways:

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#### b. Extraction of Features

This is used to decrease data from a high-dimensional space to a lower-dimensional space, i.e. a space with fewer dimensions.

## 3. Dimensionality Reduction Methods

The various methods used for dimensionality reduction include:

- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Generalized Discriminant Analysis (GDA)

Depending on the approach employed, dimensionality reduction might be linear or non-linear. The principal linear technique, known as Principal Component Analysis, or PCA, is explained more below.

## 4. Principal Component Analysis (PCA)

This method was developed by Karl Pearson. It also works on a condition. That is, data in a higher dimension space must map to data in a lower dimension space. However, the variation of the data in the lower dimensional space should be the greatest.

**It consists of the following steps:**

- Create the data’s covariance matrix.
- Compute the matrix’s eigenvectors.

Eigenvectors with the highest eigenvalues are used. That is, to recreate a substantial portion of the original data’s variance.

As a result, we have a smaller number of eigenvectors. And there may have been some data loss as a result of the process. The most significant variations, however, should be preserved by the remaining eigenvectors.

## 5. Dimensionality Reduction Techniques

Dimension reduction, in its most basic form, refers to the process of transforming a set of data. That data must be transformed from data with large dimensions into data with smaller dimensions. It must also guarantee that comparable information is conveyed concisely. However, similar approaches are used to tackle machine learning issues. The issue is obtaining better characteristics for a classification or regression job.

## 6. Dimensionality Reduction’s Importance

What is the significance of Dimension Reduction in machine learning predictive modeling?

The issue of unwanted dimension growth is strongly connected to another. That was the focus of measuring/recording data at a much finer granular level than was previously done. This is not to say that this is a new problem. It has recently gained prominence as a result of an increase in data.

Recently, there has been a significant growth in the usage of sensors in industry. These sensors continually capture data and store it for further study. There might be a lot of redundancy in the way data is gathered.

## 7. Common Techniques for Dimensionality Reduction

Dimension reduction can be accomplished in a variety of ways. The following are the most frequent methods:**a. Missing Values**

What should we do if we come across missing values when examining data? The initial step should be to determine the cause. Then, using suitable procedures, impute missing values/drop variables. But what if we have a large number of missing values? Should we fill in the blanks or drop the variables?**b. Low Variance**

Consider the following scenario: we have a constant variable (all observations have the same value, 10) in our data collection. Do you believe it has the potential to increase the model’s power? Of course not, because there is no variation.

**c. Decision Trees**

We may utilize it as a one-stop solution to a variety of problems. Missing values, outliers, and finding significant variables are just a few examples. Several data scientists have utilized decision trees and found them to be effective.**d. Random Forest **

The Random Forest algorithm is related to the decision tree algorithm. Just keep in mind that random forests have a tendency to prefer variables with a greater number of different values, i.e. numeric variables over binary/categorical values.

**e. High Correlation**

Dimensions with higher correlation can lower a model’s performance. Furthermore, having several variables with comparable information is not a desirable thing. The Pearson correlation matrix may be used to find variables having strong correlation. And then use VIF to choose one of them (Variance Inflation Factor). Variables with higher values ( VIF > 5) can be removed.

**f. Factor Analysis**

These variables can be classified based on their correlations. Each group represents a single underlying construct or component in this case. In comparison to the huge number of dimensions, these components are few in number. These variables, however, are difficult to observe.

**g. Principal Component Analysis (PCA)**

In PCA, we must transform variables into a new set of variables. Because this is a linear combination of the original variables. These additional variables are referred to as main components. Furthermore, we must get these in a certain manner. The first principle component allows for the possibility of variation in the original data, and each subsequent component has the maximum potential variance.

The second principal component must be orthogonal to the first. I There can only be two main components in a two-dimensional dataset. Here’s a look at the data and its first and second main components.

## 8. Reduce the Number of Dimensions

Dimensionality reduction provides numerous advantages in terms of machine learning.

- Overfitting is less likely in your model since it has fewer degrees of freedom. The model will be more readily generalized to new data.
- The reduction will boost the key variables if we use feature selection. It also contributes to the interpretability of your model.
- The vast majority of feature extraction algorithms are unsupervised. On unlabeled data, you can train your autoencoder or fit your PCA. This is useful if you have a large amount of unlabeled data and labeling is time-consuming and costly.

## 9. Features Selection in Reduction

Here are some ways to select variables:

- Greedy Algorithms that add and delete variables until a certain criterion is met.
- Methods of shrinking and penalization, which will increase cost for having too many variables. For example, L1 regularization will cut the coefficient of some variables to zero. Regularization restricts the space in which the coefficients can occur.
- Because we need to choose a model based on specific criteria. This must take into consideration the number of dimensions. As an example, consider the adjusted R2, AIC, or BIC. The model, unlike regularization, is not trained to optimize these criteria.
- Variables are filtered using correlation, VIF, or any kind of “distance measure” between the features.
- Filtering of variables using correlation, VIF or some “distance measure” between the features.

## 10. Advantages of Dimensionality Reduction

- Dimensionality reduction aids in data compression, resulting in less storage space.
- It reduces the computation time.
- It also aids in the removal of any redundant features.
- Dimensionality reduction helps in data compression and storage space reduction.
- It is helpful in noise removal also and as a result of that, we can improve the performance of models.
- It reduces the amount of time necessary to conduct the same computations.
- When there are fewer dimensions, there is less computation. Dimensions can also enable the use of methods that are unsuitable for a high number of dimensions.
- Reducing the dimensions of data to 2D or 3D may allow us to plot and visualize it precisely
- It handles multi-colinearity, which enhances model performance. It gets rid of features that are no longer needed. It is pointless, for example, to store a value in two distinct units (meters and inches).

## 11. Disadvantages of Dimensionality Reduction

- It may result in some data loss.
- PCA has a tendency to identify linear relationships between variables, which is not always desired.
- We may not know how many primary components to keep—in practice, certain general guidelines are followed.
- PCA fails when mean and covariance alone are insufficient to describe datasets.

## Conclusion

We have looked into Dimensionality Reduction and we believe you have learnt the basic concepts to Dimensionality Reduction, its Components, Methods, Principal Component Analysis, Features selection, Advantages, and Disadvantages of Dimensionality Reduction.

Furthermore, if you have any questions, please leave them in the comments area.