Each approach creates a view of mean, rather than a deep copy.The first approach (i.e. Im looking for an elegant way to subtract the same vector from each row of a matrix. Each of the approaches below works by adding a dimension to the mean vector, making it a 4 x 1 array, and then NumPys broadcasting takes care of the rest. with weights - just replace the vector to the right by a vector of weights. But if you did that operation on a truly sparse matrix, you would likely change the sparsity structure of the matrix (i.e. Hot Network Questions Measuring a voltage signal ranging from -2 to 2 volts into a 16 bit ADC circuit Compiler for LaTeX How do I read aloud a range of years with a slash. Sequentually subtract each value in a vector from all values in another vector. Though probably not the best solution in your case, it can be nice say if you want other pattern for the summation, e.g. Your sample data doesnt show it, because both the matrix and the vector you have chosen are dense. Subtract vector from one column of a matrix. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified. As is typical, you can do this a number of ways. Another alternative is using dot product: y x 1 -ones (numel (x)-1, 1) Here x is assumed a row vector. Principal component analysis ( PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.
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