FEATURE EXTRACTION AND PORTABILITY

INTRODUCTION

Feature Extraction : It is one of the dimensionality reduction techniques. It is a process in which an initial set of raw data is reduced to more manageable groups for processing. It extracts a subset of new features from the original feature.

Data Type Portability : It is related to the user’s ability to download the data from a platform in a format that allows the user to use it somewhere else. It allows individuals to obtain and reuse their personal data for their own purpose.

HOW IS FEATURE EXTRACTION DONE?
It aims to reduce the number of features in a dataset by creating new features from the existing ones and then discarding the original features. These new reduced set of features should then be able to summarize most of the information contained in the original set of features.

IMPORTANCE OF DATA TYPE PORTABILITY
A robust data portability system might allow regulators to contain the power of large platforms without having to take the drastic step of breaking them up. But data portability allows users to bring their history somewhere new, even if they leave or delete their data from another platform.

IMPORTANCE OF FEATURE EXTRACTION
It increases the accuracy of learned models by extracting features from the input data. This phase of the general framework reduces the dimensionality of data by removing the redundant data. Of course, it increase training and inference speed.

REAL LIFE SCENARIO
Feature Extraction
Some examples of this technique are pattern recognition and identifying common themes among a large collection of documents. One example of feature extraction that all of us can relate to is spam-detection software. If we had a large collection of emails and the keywords contained in these emails, then a feature extraction process could find correlations among the various keywords.

Data Type Portability
In social networking, data portability allows users to easily unify their contacts, exchanges, photos, videos, sound clips and personal or professional information across multiple services for example Facebook, LinkedIn and Twitter. In that way, users can have confidence that their data is current and consistent, without having to modify the content on each service’s site individually.