Linear Discriminant Analysis (LDA) is a linear model for classifying and dimension reduction. It is a technique for data categorization, dimension reduction, and visualization, most often used in pattern classification issues for feature extraction. When dealing with real-world classification issues, LDA is frequently the initial and benchmarking approach used before moving on to more sophisticated and flexible methods. With the progress of technology and trends in connected devices taking enormous amounts of data into consideration, their storage and privacy is a major worry. Linear Discriminant Analysis is a supervised classification approach that takes labels into account.
Applications of Linear discriminant analysis
LDA assists in identifying and selecting the characteristics that characterize the components of a group of customers who are highly likely to purchase comparable items. Customers’ characteristics may be obtained using a simple question and response survey. LDA assists in finding and selecting which attributes characterize a group of clients who are most likely to buy a specific item in a shopping mall.
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Face recognition is the most well-known application in the field of computer vision; each face is rendered with a huge number of pixel values. Before executing the classification job, LDA lowers the number of characteristics to a more manageable amount. Each of the newly produced dimensions is a linear combination of pixels that serves as a guideline. Fisher’s faces are the linear combinations generated by employing Fisher’s linear discriminant.
LDA is used to define the severity of a patient’s condition as mild, moderate, or severe based on different criteria and the medical therapy the patient is receiving in order to reduce treatment movement. This allows doctors to speed up or slow down their therapy. Linear Discriminant Analysis (LDA) can be used to categorize a patient’s illness as mild, moderate, or severe. The categorization is based on the patient’s different characteristics and medical trajectory.
Recent technological advancements must have resulted in the predominance of datasets with vast size, massive ordering, and complicated architecture. Such datasets encourage the extension of Linear Discriminant Analysis (LDA) into a broader field of study and development. In order to grasp the ever-changing Data Industry, We at Praxis focus on giving our students a comprehensive learning experience that ensures extensive coverage, and hands-on lab experience. Praxis is a business school with campuses in Kolkata and Bangalore that offers 9-month industry-driven Post Graduate Programs in Data Science. The PGP in Data Science with ML and AI aims to offer students with the tools, techniques, and talents required for a smooth transfer into the field of Analytics and advancement into Data Scientist positions.
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