Introduction to LDA.pdf - Search Login\/Register Introduction to Linear Discriminant Analysis in Supervised Learning Neelam Tyagi Machine Learning With

Introduction to LDA.pdf - Search Login/Register...

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Introduction to Linear Discriminant Analysis in Supervised Learning Neelam Tyagi Nov 28, 2019 Machine Learning With the advancement in technology and trends in connected-devices could consider huge data into account , their storage and privacy is a big issue to concern. Data hackers make algorithms to steal any such con±dential information from a massive amount of data. So, data must be handled precisely which is also a time-consuming task. Also, we have seen, not all the data is required for inferences, reduction in data-dimensions can also help to govern datasets that could indirectly aid in the security and privacy of data. In the core aspects of this blog, we will dwell on data dimensionality reduction techniques, it will cover the concept of Linear Discriminant Analysis(LDA), the difference of LDA with other dimension reduction technique( PCA ) and related applications. Machine Learning is divided into three vast areas named Supervised learning, Unsupervised Learning and Reinforcement Learning . In 1936, Ronald A.Fisher formulated Linear Discriminant ±rst time and showed some practical uses as a classi±er, it was described for a 2-class problem, and later generalized as ‘Multi-class Linear Discriminant Analysis’ or Multiple Discriminant Analysis ’ by C.R.Rao in the year 1948. Linear Discriminant Analysis is the most commonly used dimensionality reduction technique in supervised learning. Basically, it is a preprocessing step for pattern classi±cation and machine learning applications . It projects the dataset into moderate dimensional-space with a genuine class of separable features that minimize over±tting and computational costs. With the aim to classify objects into one of two or more groups based on some set of parameters that describes objects, LDA has come up with speci±c functions and applications, we will learn about that in detail in the coming sections. Under Linear Discriminant Analysis, we are basically looking for 1. Which set of parameters can best describe the association of the group for an object?
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