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: LSCH.m
    path of file: ./repos/MyPaperWithCode/Large-scale-spectral-clustering-using-sparse-representation-based-on-hubness
    
    the code of the file until where you have to start completion: function label = LSCH(data, k, opts)
    % label = LSC(data,k,opts): Landmark-based Spectral Clustering
    % Input:
    %       - data: the data matrix of size nSmp x nFea, where each row is a sample
    %               point
    %       - k: the number of clusters
    %       opts: options for this algorithm
    %           - p: the number of landmarks picked (default 1000)
    %           - r: the number of nearest landmarks for representation (default 5)
    %           - numRep: the number of replicates for the final kmeans (default 10)
    %           - maxIter: the maximum number of iterations for final kmeans (default 100)
    %           - mode: landmark selection method, currently support
    %               - 'kmeans': use centers of clusters generated by kmeans (default)
    %               - 'random': use randomly sampled points from the original
    %                           data set
    %           The following parameters are effective ONLY in mode 'kmeans'
    %           - kmNumRep: the number of replicates for initial kmeans (default 1)
    %           - kmMaxIter: the maximum number of iterations for initial kmeans (default 5)
    % Output:
    %       - label: the cluster assignment for each point
    % Requre:
    %       litekmeans.m
    % Usage:
    %       data = rand([100,50]);
    %       label = LSC(data,10);
    %Reference:
    %
    %	Xinlei Chen, Deng Cai, "Large Scale Spectral Clustering with
    %	Landmark-Based Representation," AAAI 2011.
    %

    
function label = LSCH(data, k, opts)
    % label = LSC(data,k,opts): Landmark-based Spectral Clustering
    % Input:
    %       - data: the data matrix of size nSmp x nFea, where each row is a sample
    %               point
    %       - k: the number of clusters
    %       opts: options for this algorithm
    %           - p: the number of landmarks picked (default 1000)
    %           - r: the number of nearest landmarks for representation (default 5)
    %           - numRep: the number of replicates for the final kmeans (default 10)
    %           - maxIter: the maximum number of iterations for final kmeans (default 100)
    %           - mode: landmark selection method, currently support
    %               - 'kmeans': use centers of clusters generated by kmeans (default)
    %               - 'random': use randomly sampled points from the original
    %                           data set
    %           The following parameters are effective ONLY in mode 'kmeans'
    %           - kmNumRep: the number of replicates for initial kmeans (default 1)
    %           - kmMaxIter: the maximum number of iterations for initial kmeans (default 5)
    % Output:
    %       - label: the cluster assignment for each point
    % Requre:
    %       litekmeans.m
    % Usage:
    %       data = rand([100,50]);
    %       label = LSC(data,10);
    %Reference:
    %
    %	Xinlei Chen, Deng Cai, "Large Scale Spectral Clustering with
    %	Landmark-Based Representation," AAAI 2011.
    %
    %   version 2.0 --Dec./2011
    %   version 1.0 --Oct./2010
    %
    %   Written by Xinlei Chen (end