%X Automatic text alignment is an important problem in natural language processing. Most researchĪbout automatic summarization revolves around summarizing news articles or scientific ItĬan be used to create the data needed to train different language models. Papers, which are somewhat small texts with simple and clear structure. The bigger theĭifference in size between the summary and the original text, the harder the problem willīe since important information will be sparser and identifying them can be more difficult. Therefore, creating datasets from larger texts can help improve automatic summarization. In this project, we try to develop an algorithm which can automatically create aĭataset for abstractive automatic summarization for bigger narrative text bodies suchĪs movie scripts.I have been studying the concept of PCA and its implementation for dimensionality reduction for more than 1 month. My goal is to classify a hyperspectral image using sparse representation by the linear combination concept which is as follow: If the ‘svd’ method is selected, this flag is used to set the parameter ‘fullmatrices’ in the singular value decomposition method. The default options perform principal component analysis on the demeaned, unit variance version of data. So consider $D$ as a dictionary with $d\times B$ dimension where $d=3000$ is the number of samples and $B=200$ is the number of band/channel. Now I am trying to construct the $D$ by this mean that the classes (sub-dictionaries) are well separated. Therefore I want to apply PCA to individual sub-dictionary in order to form the main dictionary. However, my goal is to apply PCA on hyperspectral satellite imagery like this. But my question is should I reduce the number of training pixels(observation=d$$) or reduce the variable dimension ($B$)? I have implemented the PCA in Octave and project my data on that particular low dimension. Since I use sparse representation and dictionary concept then reducing the dimension of $d$ (observation pixels for individual classes) is more make sense rather than reducing the number of features ( $B$).
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