Converting data from multi-instance to single-instance representations using p-Order Laplacian projections
Fields such as Computer Vision and Natural Language Processing have a high applicability of Machine Learning algorithms. With large amounts of complex data readily available, there are two prominent approaches to handling data complexity using Machine Learning. First, dimensionality reduction methods such as Principal Component Analysis (PCA) or Laplacian Embeddings (LE) can minimize the number of features needed to accurately represent data. This approach is often effective but has two main drawbacks. First, the input to the dimensionality reduction method is a summary of all the components ...
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