high-dimensional space
high-dimensional data
high-dimensional analysis
high-dimensional model
high-dimensional feature
be high-dimensional
high-dimensional embedding
high-dimensional representation
high-dimensional manifold
high-dimensional projection
the algorithm struggles with high-dimensional data due to the curse of dimensionality.
feature selection aims to reduce the dimensionality of the dataset.
we used pca to project the high-dimensional data into a lower-dimensional space.
high-dimensional spaces are common in image and text analysis.
the model's performance degraded significantly in high-dimensional feature space.
regularization techniques help prevent overfitting in high-dimensional models.
visualizing high-dimensional data is challenging but crucial for understanding patterns.
the dataset contained a large number of high-dimensional features.
we employed dimensionality reduction to simplify the high-dimensional representation.
sparse representations are often used to handle high-dimensional data efficiently.
the goal was to find a compact representation in a lower-dimensional space.
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