Best Feature Extraction Methods for ML and How They Work

Feature extraction

Feature extraction refers to the selection of empirically relevant features or a reduction in dimensionality to simplify and enhance the representation of features for machine learning. Commonly employed feature extraction methods favor maximizing data recovery while minimizing model complexity, resulting in a 10X increase in performance. 

As such, a host of methods exist to extract features, with their main goal being the identification of a few key pieces of information that are relevant to the outcome one seeks. For instance, face recognition is made easier through the detection of certain geometric features such as shape and size while ignoring irrelevant details such as hairstyle or eyeglass frame color. For text data, one might search for terms that correlate strongly to the class label of interest. It is also possible to extract a set of features that are highly correlated and decide whether to use all those features together or just a subset.

With such technological prowess at one’s disposal, it is tempting to think of feature extraction as a means for data cleaning. However, the true value of feature extraction is not in the process itself but rather in its impact on the model that will be built. If done properly, extracting relevant features can increase the performance of a machine learning algorithm without much effort spent on data pre-processing and cleansing.

And this is precisely what brings us to the idea of selecting the best feature extraction method for empowering the modeling process.

Understanding What Suits the Best

The best feature extraction methods for machine learning will depend on the data. The goal of many machine learning algorithms is to discover interesting aspects of data from which useful insights can be derived.

In order to do this, an algorithm must be able to estimate the number of features from a dataset, including variables that describe the attributes of an observed entity (e.g., age, mileage) and variables that describe the relationship between this entity and other individuals or entities. In this sense, a feature serves as an input to an algorithm that can be used to predict its output.

For example, linear regression is useful when predicting values based on numerical features such as average income. It is also helpful when the relationship between numerical features and a target variable is linear.

Alternatively, tree-based methods can be employed when you want to model the hierarchical nature of data. A good example would be the relationship between a parent and their child in which each child directly relates back to their parents.

SVD-based dimensionality reduction methods are designed to capture all possible interactions between variables in an attribute. Different machine learning algorithms can then use these features to predict unknown values.

Thus, it is safe to say that the best feature extraction methods are those whose features yield the greatest effect on model performance. And for this, one must understand the two fundamental points:

  1. The relationship between a target variable and its constituent attributes is key to crafting a machine learning model.
  2. Feature extraction methods should be chosen based on their ability to reduce the number of features used in computation without degrading overall performance.

The Best Methods of Feature Extraction and How They Work 

Extraction Methods for Machine Learning

 

The further sections would cover some of the go-to dimensionality reduction techniques because of their ability to extract features without significantly impacting overall performance.

1. Principal Component Analysis 

Principal Component Analysis (PCA) is a statistical method used in modeling and data compression. Specifically, it is a dimensionality reduction technique that works by transforming data into as many uncorrelated components as possible by using an orthogonal transformation such as the eigenvalue decomposition of a covariance or correlation matrix. 

PCA employs a linear transformation whose purpose is to project the data onto a new set of features containing as many variations in the original set as possible. This transformation can be performed by calculating the eigenvectors of a matrix whose entries are based on the covariance or correlation between different features in question.

Applications:

  • Image Processing
  • Medical Data Correlation
  • Facial Recognition
  • Time Series Prediction
  • Analyzing text or particular metadata fields

2. Latent Dirichlet Allocation

Latent Dirichlet Allocation (LDA) is a dimensionality reduction method that is considered state-of-the-art in modern machine learning and data mining techniques. It is useful for data clustering and topic modeling. It also works well with document classification to produce better classification quality.

The method is designed to identify a few high-level topics in documents or a set of topics that can be used later for classifying such text. Unlike PCA, LDA does not create a multivariate distribution. Instead, the algorithm provides a posterior probability distribution over words, which is represented as an unordered combination of topics (joints).

Applications:

  • Social Media Analytics
  • Text Analytics
  • Business Intelligence and Market Research
  • Sentiment Analysis
  • Recommender Systems and Online Personalization
  • Customer Analysis 

3. Independent Component Analysis

Independent Component Analysis (ICA) is a dimensionality reduction technique that works by identifying a set of statistically independent groups of variables that together capture the most variability in the data.

ICA is typically used in unsupervised data discovery methods, where a goal is to identify structure in a set of information. It is further employed as the basis for dimensionality reduction and feature extraction routines found in machine learning algorithms such as clustering, classification, regression, and outlier detection. 

It is slightly different from PCA because it does not transform data into a new set of features. Instead, it relies on the original features to be projected into a small set of independent components such as latent variables. In other words, it is based on the assumption that each variable in the dataset independently and simultaneously gives rise to its own unique contribution to the observed variability in the domain.

Applications:

  • Sentiment Analysis
  • Document Restoration
  • Image and Audio Processing
  • Gene Expression

Conclusion

The bottom line is that a good feature selection method should not sacrifice any predictive accuracy or other useful insights derived from the trained machine learning algorithm. And to be considered a feature, each variable should have a significant impact on prediction. As it turns out, dimensionality reduction presents itself as a viable option to reduce the number of input variables and the computational complexity of the learned models.

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