Doc Editor - PCA

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that quest is the best if you dont think so then we have different opinions hello Im Josh stommer and welcome to stat quest today were gonna be talking about the main ideas behind principle component analysis and were going to cover those concepts in five minutes if you want more details than you get here be sure to check out my other PCA video lets say we had some normal cells if youre not a biologist imagine that these could be people or cars or cities or etc they could be anything even though they look the same we suspect that there are differences these might be one type of cell or one type of person or car or city etc these might be another type of cell and lastly these might be a third type of cell unfortunately we cant observe differences from the outside so we sequence the messenger RNA in each cell to identify which genes are active this tells us what the cell is doing if they were people we could measure their weight blood pressure reading level etc okay heres the da

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What can I use instead of PCA?

Linear Discriminant Analysis (LDA) Linear discriminant analysis (LDA) not to be confused with latent Dirichlet allocation also creates linear combinations of your original features. However, unlike PCA, LDA doesnt maximize explained variance. Instead, it maximizes the separability between classes.

Who should not use PCA?

While it is technically possible to use PCA on discrete variables, or categorical variables that have been one hot encoded variables, you should not. Simply put, if your variables dont belong on a coordinate plane, then do not apply PCA to them.

How to do PCA manually?

Steps Involved in the PCA Step 1: Standardize the dataset. Step 2: Calculate the covariance matrix for the features in the dataset. Step 3: Calculate the eigenvalues and eigenvectors for the covariance matrix. Step 4: Sort eigenvalues and their corresponding eigenvectors.

How do you make a PCA from scratch?

Steps to implement PCA in Python Subtract the mean of each variable. Calculate the Covariance Matrix. Compute the Eigenvalues and Eigenvectors. Sort Eigenvalues in descending order. Select a subset from the rearranged Eigenvalue matrix. Transform the data.

How to make PCA in Python?

PCA to Speed-Up Machine Learning Algorithms Step 1: Download and Load the Data. Step 2: Split Data Into Training and Test Sets. Step 3: Standardize the Data. Step 4: Import and Apply PCA. Step 5: Apply the Mapping (transform) to the Training Set and the Test Set. Step 6: Apply Logistic Regression to the Transformed Data.

What are the assumptions to be considered prior to applying PCA?

The assumptions in PCA are: There must be linearity in the data set, i.e. the variables combine in a linear manner to form the dataset. The variables exhibit relationships among themselves.

What are the limitations of principal component analysis?

Low interpretability of principal components. Principal components are linear combinations of the features from the original data, but they are not as easy to interpret. For example, it is difficult to tell which are the most important features in the dataset after computing principal components.

On what type of data does PCA fail?

When a given data set is not linearly distributed but might be arranged along with non-orthogonal axes or well described by a geometric parameter, PCA could fail to represent and recover original data from projected variables.

What can I use instead of PCA for clustering?

Just like PCA, t-SNE takes high-dimensional data and reduces it to a low-dimensional graph (2-D typically). It is also a great dimensionality reduction technique. Unlike PCA, t-SNE can reduce dimensions with non-linear relationships.

What is alternative method to PCA?

In dimensionality reduction, RBM performs better than PCA: the classification results of RBM+ELM (i.e. the extreme learning machine) is higher than those of PCA+ELM. This shows that RBM can extract the spectral features more efficiently than PCA. Thus, RBM is a good alternative method for PCA in spectral processing.

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Improve your day-to-day document routines with Doc Editor - PCA

Use Doc Editor - PCA using these basic steps

PDF editing simplified with DocHub

Drive efficiency with the DocHub add-on for Google Workspace

How to Doc Editor - PCA