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Results tagged with pca
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user 17672
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
4
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Basic question on PCA: can it be used to divide original features into groups?
Consider the goal of PCA (definition from wikipedia)
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly … So PCA takes your 100 variables and gives you 100 new variables that are not correlated. Suppose too that two new variables explain 90% of the variation. …
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Algorithm and R implementation of sparse PCA
Another good package is the elasticnet package that Zou and Hastie put out. It has the function spca.
Be careful to select a good value of $\lambda$, the sparsity parameter (or vector of them).
I wo …
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3
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How to compute classification accuracy of PCA?
It may first make sense to think about how PCA can be used to classify
observations in this setting.
PCA is not in itself a classification method. … SIMCA uses a PCA model fitted to each group to determine the distance of the new observation that must be classified to each of the group's respective PCA models. …
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How many components to use when doing a PLS regression?
Selecting the number of components for PLS is a bit trickier than for PCA. …
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Identify original features corresponding to high singular/principal component values
These are the COEFF output from PCA. The elements of the eigenvectors are the coefficients in the linear relationships defined by PCA. … A better approach would be to try a sparse PCA method. Two are: SCoTLASS, detailed in the paper below:
Jolliffe, I. T., Trendafilov, N. T., and Uddin, M. (2003). …
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What are good metrics to assess the quality of a PCA fit, in order to select the number of c...
There are other ways to evaluate how good your PCA model is if you know more about the data. … It is not usable in real data cases though, where you don't know the true PCA loadings. …
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Importance of multivariate normality assumption for BIC-like sparse model selection inferenc...
and an unconstrained PCA. … An assumption of of PCA is that the data are jointly normally distributed. …
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PCA Transformed data and regression
The practice of using the scores of the PCA model (the lower-dimensional variables you can get from PCA) is known as Principal Component Regression (PCR). … Both PLS and PCA can create problems of interpretation since they transform your original data into new variables. …
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