pca Definition and Topics - 7 Discussions

The Pitcairn PCA-2 was an autogyro developed in the United States in the early 1930s. It was Harold F. Pitcairn's first autogyro design to sell in quantity. It had a conventional design for its day – an airplane-like fuselage with two open cockpits in tandem, and an engine mounted tractor-fashion in the nose. The lift by the four-blade main rotor was augmented by stubby, low-set monoplane wings that also carried the control surfaces. The wingtips featured considerable dihedral that acted as winglets for added stability.

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  1. Lecture 5a - Pandemic Pedantics - Derivation of PCA and Kernel PCA

    Lecture 5a - Pandemic Pedantics - Derivation of PCA and Kernel PCA

    Here we talk about how we come to the formulas for PCA and Kernel PCA. We briefly introduce kernel functions, and talk about feature spaces. This builds on the introductory lecture for PCA and also that for Kernel PCA.
  2. Lecture 5 - Science, Toys, and the PCA

    Lecture 5 - Science, Toys, and the PCA

    We open this lecture with a discussion of how advancements in science and technology come from a consumer demand for better toys. We also give an introduction to Principle Component Analysis (PCA). We talk about how to arrange data, shift it, and the find the principle components of our dataset.
  3. Lecture 2 - Understanding Everything from Data - The SVD

    Lecture 2 - Understanding Everything from Data - The SVD

    In this video I give an introduction to the singular value decomposition, one of the key tools to learning from data. The SVD allows us to assemble data into a matrix, and then to find the key or "principle" components of the data, which will allow us to represent the entire data set with only a few
  4. C

    A Has anybody programmed the mcKL expansion?

    Hello everyone. I am trying to implement the mcKL expansion proposed in this article using Matlab and two vectors of correlated data of size 1000*50, meaning 50 realizations of two random processes measured 1000 times. As the article says, if two stochastic processes are correlated, one cannot...
  5. C

    I Differences between the PCA function and Karhunen-Loève expansion

    Hello everyone. I am currently using the pca function from matlab on a gaussian process. Matlab's pca offers three results. Coeff, Score and Latent. Latent are the eigenvalues of the covariance matrix, Coeff are the eigenvectors of said matrix and Score are the representation of the original...
  6. R

    I Using PCA for variable reduction

    In the textbook “Principal Component Analysis” Jolliffe (§9.2) suggests the following method for variable reduction: “When the variables fall into well-defined clusters, there will be one high-variance PC and, except in the case of 'single-variable' clusters, one or more low-variance PCs...
  7. D

    Java Eigenword embeddings and spectral learning; I'm a beginner...

    Hi everyone, I am a mathematics undergraduate and I'm currently doing an internship at the informatics department of a university. I am well and truly out of my depth. My supervisor has assigned me tasks which include Java (a language I'm having to quickly pick up, having only used python/R)...