Dimensionality Reduction: Streamlining Data Pre-Processing for Data Scientists

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.

These dimensionality reduction methods essentially fall under the category of data pre-processing, which is done before model training.

Dimensionality Reduction

A dataset’s dimensionality describes the number of input features, variables, or columns it contains, while dimensionality reduction describes the process of lowering these features.

Predictive modeling is more difficult because a dataset frequently comprises many input features. It isn’t easy to visualize or forecast the training dataset with many features in these circumstances. Hence, dimensionality reduction techniques must be applied.

“It is a way of turning the higher dimensions dataset into lesser dimensions dataset while guaranteeing that it gives similar information” is how the term “dimensionality reduction technique” is defined. Machine learning frequently employs these methods to produce a more accurate predictive model while addressing classification and regression issues.

It is frequently applied to high-dimensional data domains, including speech recognition, signal processing, bioinformatics, etc. Moreover, it can be utilized for cluster analysis, noise reduction, and data visualization.

High-dimensional data problem

• It may imply a significant computational cost for learning
• While learning a model, it frequently results in over-fitting, which causes the model to perform well on the training data but poorly on the test data
• High-dimension data are rarely randomly distributed and highly correlated, frequently with erroneous correlations
• Certain technologies for distance-based analysis may be less accurate at high dimensions because the distances between the closest and farthest data points can equalize

Importance of Dimensionality Reduction

1. Your machine learning data will benefit from dimensionality reduction in several ways, including:

• Less complexity results from fewer features.
• Since there are fewer data, we will require less storage.
• Less features necessitate faster calculation.
• Less misleading data results in improved model accuracy.
• Less data means faster algorithm training.
• Data visualization is made quicker by reducing the feature dimensions of the data set.
• It eliminates noise and unnecessary features.

2. The dimension reduction approach can be used in one of two ways, as listed below:

• Feature Selection: To create a high accuracy model, a subset of the important features from a dataset must be chosen, and the irrelevant characteristics must be excluded. This process is known as feature selection. Put another way, and it is a method of choosing the best characteristics from the input dataset.

The feature selection process employs three techniques:

1. Filter techniques: This technique reduces the data set into a useful subset.
2. Wrapper Techniques: The performance of the features fed into this method is evaluated using a machine learning model. Whether preserving or removing features to increase the model’s accuracy is preferable depends on performance. While more involved than filtering, this method is more accurate.
3. Embedded procedures: The embedding process examines the numerous training iterations of the machine learning model and rates the significance of each feature.

• Feature Extraction: The process of converting a space with several dimensions to one with fewer dimensions is known as feature extraction. This strategy is helpful when retaining all the information while processing it with fewer resources.

Typical feature extraction methods include:

1. Principal Component Analysis: A method known as principal component analysis, or PCA, reduces the number of dimensions in large data sets by condensing a huge collection of variables into a smaller set while retaining most of the large set’s information.
2. Linear discriminant analysis: LDA is frequently used to reduce dimensionality with continuous data. LDA projects the data in the direction of rising variance while rotating it. Principal components are characteristics that exhibit the greatest variance.
3. Kernel PCA: A nonlinear extension of PCA, this method is effective for more complex structures that are difficult or inappropriate to describe in a linear subspace. KPCA creates nonlinear maps by applying the “kernel trick”.
4. Quadratic discriminant analysis: Using this method, data is projected to maximize class separability. The projection places instances from the same class closely together while spacing out examples from different classes.

Advantages Of Dimensionality Reduction

• Less storage space is needed because it helps with data compression.
• It expedites the computation.
• It also helps to get rid of any unnecessary features.

Disadvantages Of Dimensionality Reduction

• The dimensionality reduction technique resulted in some data loss, which may influence the efficiency of upcoming training algorithms.
• Perhaps a lot of processing power is required.
• It may be difficult to interpret traits that have been altered.
• As a result, it becomes more challenging to understand the independent variables.

Services in AWS to reduce Dimensionality

• In AWS, we can use Amazon SageMaker, a fully managed machine learning service, to reduce the dimensionality of your dataset. Amazon SageMaker provides built-in algorithms for dimensionality reduction, such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF), that can help you reduce the number of features while keeping as much information in your dataset.
• Alternatively, we can use Amazon Elastic MapReduce (EMR) to reduce dimensionality using Hadoop and Spark. EMR provides pre-built Amazon Machine Images (AMIs) with Hadoop and Spark, and we can install libraries such as Apache Mahout or MLlib to reduce dimensionality on your dataset.
• It’s important to note that the specific service you choose will depend on the requirements of your use case and the type of data you’re working with.

Conclusion

Every second, enormous amounts of data are created. So, analyzing them accurately and with the best possible resource allocation is equally crucial. Dimensionality Reduction techniques make data pre-processing precise and effective, which is why they are a great solution for data scientists.

Drop a query if you have any questions regarding Dimensionality Reduction and we will get back to you quickly.

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