high-dimensional

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.