Recall that the general procedure for PCA is: Recenter each feature of the dataset by subtracting that feature's mean from the feature vector Calculate the covariance matrix for your centered dataset ...

Introduction Now that you've gotten a high-level overview of the use cases for PCA and some general notes regarding the algorithm's implementation, its time to dive deeper into the theory behind PCA.

Abstract: In this paper, a new calculation method of adaptive weight for space-time adaptive processing (STAP) is proposed by using the pulse order-based inverse covariance matrix recursion. The ...

Abstract: This work is dedicated to the calculation of asymptotic covariance matrices of M -estimates in the Ozaki model, using the Huber function as an example. M-estimation methods generalize the ...