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import numpy as np
from sklearn.covariance import EllipticEnvelope

# Initialize outlier detection model
outlier_detector = EllipticEnvelope(contamination=0.1)

# Function to detect outliers on new data
def detect_outliers(new_data):
  # Make predictions on new data
  outlier_detector.fit(data)
  y_pred = outlier_detector.predict(new_data)
  
  # Identify predictions as outliers
  outliers = new_data[y_pred == -1]
  
  # Return the outlier data points
  return outliers

# Example usage
data = [...] # initialize original training data

# Get new data
new_data = get_new_data() 

# Detect outliers
outliers = detect_outliers(new_data)

# Outliers contains the new data points considered outliers