As you sail through and level up in your academic journey, you will find that the topics are getting more and more convoluted. Now, it is essential for you to grasp those convoluted topics so that it makes your journey to success a lot less daunting.
Speaking of convoluted topics, Linear Discriminant Analysis is definitely one. In fact, it is highly likely that this will be one of those topics to add to your worries. If this happens to be a concern for you, these insights will assist you to nail a paper on the topic.
Linear Discriminant Analysis is a form of the dimensionality reduction process. This is implemented as a preprocessing step in pattern classification applications and Machine Learning. The objective of dimensionality reduction methods is to minimize the dimensions. It eliminates the dependent and recurring features by transforming them from higher dimensional space to lower dimensions.
Linear Discriminant Analysis is a supervised classification technique that takes the labels into consideration. This variant of dimensionality reduction is integrated into bioinformatics, chemistry, and biometrics.
There are two different approaches to Linear Discriminant Analysis. The approaches are as follows.
The following are some of the functions involved in the process of Linear Discriminant Analysis.
Discriminant analysis has been efficiently applied in many different fields. The method can be applied as long as you can transform the problem into a classification problem. You can employ Discriminant Analysis for authentic applications if you have a new additional combination of objects and features thathasn't been used by other people before. The following are some examples of the application of LDA in various fields.
Political scientists who study and evaluate court case dispositions often adopt the methods derived from this analysis. These methods are also adapted to determine the voting behavior among legislators or among the citizens.
Discriminant analysis methods are effective in determining the admissions to a specific education program. Psychologists working on educational testing determine which students will be successful, depending on the differences in different variables.
Many researchers have employed discriminant analysis in a huge variety of social science studies. In social sciences, these techniques have been implemented in psychological and educational testing. Another prominent use of this method is seen in personnel testing.This method is helpful in evaluating experimental data when assigned to a ‘treatment’ group. It is presumed to impact the scores on different variables.
In the process of computerized face recognition, each face is characterized by a large number of pixel values. Linear discriminant analysis is specifically adopted here to lower the number of features to a more manageable figure before classification.Each new dimension tends to be a linear combination of pixel values, which come together as a template. The linear combinations derived using Fisher's linear discriminant are recognized as Fisher faces, while those attained using the related principal component analysis are known as Eigenfaces.
The major application of discriminant analysis in the sphere of medicine lies in the evaluation of the severity state of a patient and prognosis of disease outcome. For instance, during retrospective analysis, patients are categorized into sections depending on the severity ofdisease- mild, moderate and severe. Then, the results of laboratory and clinical analyses are examined to highlight the variables that are statistically distinct in studied groups.With the help of these variables, discriminant functions are created. This helps in classifying a disease in a future patient into mild, moderate or severe form.
In marketing, Discriminant Analysis was once frequently used to identify the factors that distinguish multiple types of products and/or customers on the basis of surveys or other types of collected data. Nowadays, logistic regression or other methods are now more commonly applied.
LDA was the first statistical technique applied in bankruptcy prediction. It depended on the accounting ratios and other financial variables. It helped to determine the companies that will enter bankruptcy and the ones that will survive.The technique had its limitations that include known non-conformance of accounting ratios to the normal distribution assumptions of LDA. However, Edward Altman's 1968 model is still a predominant technique when it comes to practical applications.Parting thoughts,It is quite normal to feel overwhelmed with a complicated topic like Linear Discriminant Analysis. However, with these detailed insights, dealing with this topic would be a breeze. This way, you won’t have to settle for a grade that fails to satisfy you.
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