n-dimensional

the dataset was analyzed using n-dimensional scaling to reduce complexity.

we explored the data in n-dimensional space to identify potential clusters.

the model represents data points in an n-dimensional feature space.

visualizing data in n-dimensional space can be challenging but insightful.

the algorithm projects high-dimensional data into a lower n-dimensional representation.

understanding n-dimensional geometry is crucial for advanced data analysis.

the researchers used n-dimensional embeddings to capture semantic relationships.

we applied principal component analysis to reduce the data to n-dimensional components.

the n-dimensional hypercube is a useful concept in topology.

the system maps users into an n-dimensional preference space.

we need to consider the curse of dimensionality in n-dimensional data.