You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: tsne.m
    path of file: ./repos/HOG-Pedestrian-Detector/libs/t-sne
    
    the code of the file until where you have to start completion: function ydata = tsne(X, labels, no_dims, initial_dims, perplexity)
%TSNE Performs symmetric t-SNE on dataset X
%
%   mappedX = tsne(X, labels, no_dims, initial_dims, perplexity)
%   mappedX = tsne(X, labels, initial_solution, perplexity)
%
% The function performs symmetric t-SNE on the NxD dataset X to reduce its 
% dimensionality to no_dims dimensions (default = 2). The data is 
% preprocessed using PCA, reducing the dimensionality to initial_dims 

    
function ydata = tsne(X, labels, no_dims, initial_dims, perplexity)
%TSNE Performs symmetric t-SNE on dataset X
%
%   mappedX = tsne(X, labels, no_dims, initial_dims, perplexity)
%   mappedX = tsne(X, labels, initial_solution, perplexity)
%
% The function performs symmetric t-SNE on the NxD dataset X to reduce its 
% dimensionality to no_dims dimensions (default = 2). The data is 
% preprocessed using PCA, reducing the dimensionality to initial_dims 
% dimensions (default = 30). Alternatively, an initial solution obtained 
% from an other dimensionality reduction technique may be specified in 
% initial_solution. The perplexity of the Gaussian kernel that is employed 
% can be specified through perplexity (default = 30). The labels of the
% data are not used by t-SNE itself, however, they are used to color
% intermediate plots. Please provide an empty labels matrix [] if you
% don't want to plot results during the optimization.
% The low-dimensional data representation is returned in mappedX.
%
%
% (C) Laurens van der Maaten, 2010
% University of California, San Diego


    if ~exist('labels', 'var')
        labels = [];
    end