QSNE1.0Fast nonlinear dimensionality reduction using a quadratic-convergence t-SNE algorithm.Antti HakkinenMultivariate Statisticsliblapack3qsne
install.bashInput matrix. Rows represent samples and columns the input variables. All variables are used in the mapping.(Optional) initial guess. Rows represent samples and columns the projected variables. If a file is not provided, an initial guess is derived using a truncated SVD of the input matrix (PCA).Output dimension. Values of 2 and 3 are useful for visualization, but any number can be used.Perplexity. Controls roughly the number of neighbor samples affecting each sample.Perplexity range. An optimal perplexity is automatically sought in the range [p-r/2,p+r/2] where p is the specified perplexity and r is the range.Maximum number of iterations to perform. q-SNE requires roughly sqrt(n) iterations compared to a regular t-SNE implementation.Objective tolerance for detecting a stall in optimization. This allows q-SNE to stop early, when the objective no longer decreases.Rank of the approximate Hessian. A value of 0 implies plain gradient ascent (the original t-SNE algorithm) and larger values are required for quadratic convergence. Should be roughly the local inherent dimension.Number of threads executing in parallel. By default, a single thread for each core is used.t-SNE compatibility mode. Causes q-SNE to switch to gradient ascent and scales variables to those used in van der Maaten's t-SNE implementation. Note that in this mode, the convergence is linear.