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Variations of PCA can still be applicable. If the data is not linear, use nonlinear PCA. It is not an issue there a multiple "y"s for every "x". PCA is unsupervised, there is no notion of targets. … In PCA, there are only dimensions. Typically, the dimensions are standardized so each dimension can be weighted independent of scale. One caveat - PCA is domain agnostic. …

The PCA projections do not look not orthogonal because your figure axes are not equal. …

From scikit-learn's PCA docs: When True (False by default) the components_ vectors are multiplied by the square root of n_samples… That creates an overflow issue which results in the ValueError …

It is not the best use of Principal Component Analysis (PCA) to compare information loss for different data structures. It would be more appropriate to use Information Theory. …

There is no theoretical upper bound for t-SNE. However, pragmatically it will become increasingly computationally impracticable to reduce higher and higher dimensions to lower and lower dimensions. Th …

Graph comparisons can be tricky. One option is to take an Information Theory approach, something like "An information-theoretic, all-scales approach to comparing networks"

One option is to use Multidimensional scaling (MDS) for dimensionality reduction. MDS can create a visualization of the relative positions for data based on the distance between the data points. In y …

No. t-Distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) are dimension reduction techniques, aka fewer columns of a tidy dataframe. …

That is probably a result of a floating point error. The matrix is 60,000 x 20 and sparse (mostly zeros). The result of the calculations are values very close to zero that are not correctly represente …

Principal Component Analysis (PCA) is not very useful for that type of interpretation. …

No - There is no need to normalize after Principal Component Analysis (PCA) because each dimension is on the same scale. …

Principal component analysis (PCA) is not designed for time series data. It would be better to switch to singular spectrum analysis (SSA) which is designed for time series. …

The paper is incorrect in applying Principal Component Analysis (PCA) to boolean data since PCA implicitly minimizes a squared loss function, which is not always appropriate for not real-valued data. …

There is principal component analysis (PCA) in Spark's Machine Learning Library (MLlib). …

In scikit-learn, PCA has the fit_transform method which fits and applies the dimensionality reduction to the training data. There is also transform which only applies the dimensionality reduction. …