outlier reduction

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### Desribe the bug

Whenever I perform outlier reduction (regardless of the outlier reduction strategy), meaningless feature words such as 'the', 'end', 'of' frequently appear among the top 10 feature words for each topic. Why is this happening? When I don’t perform outlier reduction, the representative feature words are displayed correctly."

### Reproduction

```python
from bertopic import BERTopic

```
embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = embedding_model.encode(data['document'], show_progress_bar=False)

# Step 2 - Reduce dimensionality
umap_model = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine',random_state=42)#
# Step 3 - Cluster reduced embeddings
#cluster_model = KMeans(n_clusters=15)
hdbscan_model = HDBSCAN(min_cluster_size=10, metric='euclidean', cluster_selection_method='eom', prediction_data=True, min_samples=5)
from sklearn.feature_extraction.text import CountVectorizer
# Step 4 - Tokenize topics
vectorizer_model = CountVectorizer(ngram_range=(1, 1),stop_words="english",min_df=2)#,
# Step 5 - Create topic representation
ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True)
representation_model = MaximalMarginalRelevance(diversity=0.2,top_n_words=15)#

#representation_model = KeyBERTInspired()
topic_model = BERTopic(
top_n_words=16,
embedding_model=embedding_model,
representation_model=representation_model,
umap_model=umap_model,
hdbscan_model=hdbscan_model,
vectorizer_model=vectorizer_model,
ctfidf_model=ctfidf_model,
calculate_probabilities=True,
nr_topics="auto"
)
topics, probs= topic_model.fit_transform(data['document'],embeddings)

print(topic_model.get_topic_info())

new_topics = topic_model.reduce_outliers(data['document'],topics, strategy="c-tf-idf", threshold=0.1)
new_topics = topic_model.reduce_outliers(data['document'], new_topics, strategy="distributions")

topic_model.update_topics(data['document'], topics=new_topics)
Whenever I perform outlier reduction (regardless of the outlier reduction strategy), meaningless feature words such as 'the', 'end', 'of' frequently appear among the top 10 feature words for each topic. Why is this happening? When I don’t perform outlier reduction, the representative feature words are displayed correctly."

### BERTopic Version

1.6.1

Start with the supplied Python reproduction, focusing on reduce_outliers with the c-tf-idf and distributions strategies and the subsequent update_topics call. Compare topic representations before and after these steps, then determine whether the stop-word and meaningless-word regression is reproducible and what change or documented limitation explains it.

Written by the indexing model from the issue text.