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Tag: Principal Components Analysis

Data Analysis

Dimensionality Reduction – (PCA)

October 31, 2018October 18, 2020 2 Comments

Variations in the dataset is actually the information from the dataset and this is what the PCA uses. In simple terms PCA or Principal component analysis is a process to emphasise variations in a data set and generate strong pattern out of it. We can figure out the whole concepts in 3 points as follows— […]

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Data Analysis

Eigenvector represents greatest variance in case of PCA

October 31, 2018October 17, 2020 2 Comments

In case of Principal Component Analysis we project our data points on a vector in a direction of maximum variance to decrease the number of existing components. In this case we consider the direction eigenvector generated using covariance matrix as the direction of maximum variance. In this article we look into the proof of why […]

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Data Analysis

Eigen Vector and Eigen Values

October 30, 2018October 17, 2020 1 Comment

Eigen vector is the direction in a coordinate space defined by a metrics which doesn’t change its direction with metrics transformation. Eigen value is a scaler number which is multiplied with Eigen vector to give same result as Eigen vector multiplier with existing metrics. Editorial Team

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