title: T-SNE can not convert high dimension data to more than 4 dimension
body: <p>I wanted to use T-sne features for DBSCAN clustering algorithm, but sklearn implementation is not running for n_components&gt;4.</p>
  <pre><code>from sklearn.manifold import TSNE
  X = np.array([[0, 0, 0,2, 0, 0,2], [0, 1, 1,53, 0, 0,2], [1, 0, 1,12, 0, 0,2], [1, 1, 1,75, 0, 0,2]])
  X_embedded = TSNE(n_components=5).fit_transform(X)
  </code></pre>
  <p>Error:</p>
  <pre><code>ValueError                                Traceback (most recent call last)
  &lt;ipython-input-22-79c671f39a06&gt; in &lt;module&gt;
  ----&gt; 1 tsne_data = model.fit(clustering_ready_data_encoded)
  
  ~/anaconda3/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py in fit(self, X, y)
      902         y : Ignored
      903         &quot;&quot;&quot;
  --&gt; 904         self.fit_transform(X)
      905         return self
  
  ~/anaconda3/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py in fit_transform(self, X, y)
      884             Embedding of the training data in low-dimensional space.
      885         &quot;&quot;&quot;
  --&gt; 886         embedding = self._fit(X)
      887         self.embedding_ = embedding
      888         return self.embedding_
  
  ~/anaconda3/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py in _fit(self, X, skip_num_points)
      685 
      686         if self.method == 'barnes_hut' and self.n_components &gt; 3:
  --&gt; 687             raise ValueError(&quot;'n_components' should be inferior to 4 for the &quot;
      688                              &quot;barnes_hut algorithm as it relies on &quot;
      689                              &quot;quad-tree or oct-tree.&quot;)
  
  ValueError: 'n_components' should be inferior to 4 for the barnes_hut algorithm as it relies on quad-tree or oct-tree.
  </code></pre>
  <p>I know T-sne is not preferred for features in Clustering algorithm but I want to still try.</p>