Lower-dimensional Representation


Lower-dimensional representation refers to the process of reducing the number of dimensions in a dataset while retaining as much of the original information as possible. This is achieved through techniques such as Principal Component Analysis (PCA), t-SNE, and Autoencoders. Lower-dimensional representation is useful in data visualization, as it allows for the visualization of high-dimensional data in a more easily interpretable format. It is also useful in machine learning, as it can improve the performance of models by reducing the number of features and removing noise from the data.


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