Dimensionality Reduction Techniques in Data Science

 

Introduction

Machine learning involves a lot of computations and resources, not to mention the manual effort that goes along with it, to analyze data using a list of variables. The dimensionality reduction approaches are instrumental in this situation. 

A high-dimensional dataset may be converted into a lower-dimensional dataset using the dimensionality reduction approach without sacrificing any of the critical characteristics of the original data. These dimensionality reduction methods essentially fall under data pre-processing, which is done before model training.

What is Dimensionality Reduction Technique in Data Science?

Consider developing a model that can forecast the weather for the following day using the current climatic circumstances. Millions of such environmental characteristics are too difficult to examine, including sunshine, humidity, cold, temperature, and many more that might influence the current conditions. Therefore, by identifying the features with a high degree of correlation and grouping them together, we may reduce the number of features.

What justifies the necessity of dimension reduction?

Machine learning and deep learning algorithms input enormous amounts of data to learn about variations, trends, and patterns. Unfortunately, the fact that there are so many characteristics in such large data sets sometimes results in the dimensionality curse.

Additionally, sparsity occurs often in large datasets. When features with little or no value are used in a training model, they result in subpar performance when tested. This is known as sparsity. Such duplicate characteristics also make it difficult to group comparable data features. Machine learning and deep learning techniques can be learned well through machine learning courses in Bangalore.

Therefore, dimensionality reduction strategies are used to combat the dimensionality curse. Why dimensionality reduction is helpful is a question that has the following answers:

• Since duplicate data will be deleted, there will be less opportunity for assumption, which will improve the model's performance.

• Utilizing fewer computational resources will save time and money.

• A difficulty that will be resolved once the dimension is reduced is that some machine learning and deep learning approaches do not perform well on high-dimensional data.

• Since clustering clean and non-sparse data is simpler and more reliable, these data will produce more statistically significant findings.

What are the techniques for reducing dimensions?

Linear Methods

• PCA

One DR approach in data science is principal component analysis (PCA). Take into account a group of 'p' variables that are connected with one another. This method condenses the set of variables with the letter "p" into a smaller group of uncorrelated variables with the letter "k," where (KP). Principal components are these 'k' variables, which vary in a way comparable to the original dataset.

PCA uses the correlation between the features that it combines to determine which features are related. The dataset that is produced as a consequence includes fewer characteristics that are linearly associated with one another.

• Factor Analysis

It is a development of Principal Component Analysis (PCA). This method's primary goal is not only to minimize the dataset. It focuses more on identifying hidden variables, which are outcomes of other dataset variables. A single variable does not specifically measure them.

Factors are another name for latent variables. Therefore, factor analysis refers to the process of creating a model that assesses these latent variables.

• Linear Discriminant Analysis

It is a dimensionality reduction method that is mostly employed for supervised classification issues. In multi-classification, logistic regression is ineffective. LDA enters the scene as a solution to address that flaw. 

It effectively distinguishes between training variables inside each of their classes. Additionally, it differs from PCA in that it computes a linear combination of the input characteristics to enhance the distinction between various classes.

• SVD

Think about some data with 'm' columns. These "m" columns (features) are projected onto a subspace with "m" or fewer columns using the Truncated Singular Value Decomposition technique (TSVD), which preserves the properties of the data.

An example of a dataset where TSVD can be applied is one with reviews of items sold on the internet. TSVD effectively handles null values in the data caused by the review column being largely left blank. The TruncatedSVD() function makes it simple to use this approach.

Non-Linear Methods

• Kernel PCA

For datasets that can be linearly separated, PCA is surprisingly effective. The decreased dimension of the dataset might not be correct, though, if we use it on non-linear datasets. Thus, this is the point at which Kernel PCA is effective.

After undergoing a kernel function, the dataset is briefly projected into a higher dimensional feature space. In this case, the classes are changed and may be recognized and divided linearly with the aid of a straight line.

• T-Distributed Stochastic Neighbor Embedding

It is a non-linear dimensionality reduction technique mostly used in NLP, image processing, and data visualization. The parameter "perplexity" in T-SNE is adjustable. It demonstrates how to keep track of the dataset's global and local components. It provides a rough estimate of how many near neighbors each data point has. 

The Kullback-Leibler divergence between the joint probability of the low-dimensional embedding and high-dimensional datasets is reduced, and it also converts the similarities between various data points into joint probabilities.

• Multidimensional Scaling

By reducing the data to a smaller dimension, a process known as scaling, the data is made simpler. It is a non-linear dimensionality reduction method that visually illustrates the gaps or differences between the collections of characteristics. Shorter distances are considered comparable, whereas longer distances are considered distinct.

• Isometric Mapping (Isomap)

It is a non-linear dimension reduction method that is essentially a kernel PCA or MDS extension. Linking every feature based on the curved or geodesic distances between their closest neighbors lowers dimensionality.

Conclusion

Every second, a massive amount of data is produced. Therefore, it is equally crucial to analyze them accurately and with the best use of available resources. Techniques for dimensionality reduction support precise and effective data pre-processing. To become a competent data scientist, visit the top data science course in Bangalore, designed in partnership with IBM.