code: def mcfs(X, n_selected_features, **kwargs):
      """
      This function implements unsupervised feature selection for multi-cluster data.
  
      Input
      -----
      X: {numpy array}, shape (n_samples, n_features)
          input data
      n_selected_features: {int}
          number of features to select
      kwargs: {dictionary}
          W: {sparse matrix}, shape (n_samples, n_samples)
              affinity matrix
          n_clusters: {int}
              number of clusters (default is 5)
  
      Output
      ------
      W: {numpy array}, shape(n_features, n_clusters)
          feature weight matrix
  
      Reference
      ---------
      Cai, Deng et al. "Unsupervised Feature Selection for Multi-Cluster Data." KDD 2010.
      """
  
      # use the default affinity matrix
      if "W" not in kwargs:
          W = construct_W(X)
      else:
          W = kwargs["W"]
      # default number of clusters is 5
      if "n_clusters" not in kwargs:
          n_clusters = 5
      else:
          n_clusters = kwargs["n_clusters"]
  
      # solve the generalized eigen-decomposition problem and get the top K
      # eigen-vectors with respect to the smallest eigenvalues
      W = W.toarray()
      W = (W + W.T) / 2
      W_norm = np.diag(np.sqrt(1 / W.sum(1)))
      W = np.dot(W_norm, np.dot(W, W_norm))
      WT = W.T
      W[W < WT] = WT[W < WT]
      eigen_value, ul = scipy.linalg.eigh(a=W)
      Y = np.dot(W_norm, ul[:, -1 * n_clusters - 1 : -1])
  
      # solve K L1-regularized regression problem using LARs algorithm with cardinality constraint being d
      n_sample, n_feature = X.shape
      W = np.zeros((n_feature, n_clusters))
      for i in range(n_clusters):
          clf = linear_model.Lars(n_nonzero_coefs=n_selected_features)
          clf.fit(X, Y[:, i])
          W[:, i] = clf.coef_
      return W
  
imports: import scipy
  import numpy
  from sklearn import linear_model
  from skfeature.utility.construct_W import construct_W
label_desc: Use image node_id 1 for calling a global function with example usage: mcfs(X, n_selected_features) and returns: W