2,907 research outputs found
Universality laws for randomized dimension reduction, with applications
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space to facilitate its analysis. In the Euclidean setting, one fundamental technique for dimension reduction is to apply a random linear map to the data. This dimension reduction procedure succeeds when it preserves certain geometric features of the set.
The question is how large the embedding dimension must be to ensure that randomized dimension reduction succeeds with high probability.
This paper studies a natural family of randomized dimension reduction maps and a large class of data sets. It proves that there is a phase transition in the success probability of the dimension reduction map as the embedding dimension increases. For a given data set, the location of the phase transition is the same for all maps in this family. Furthermore, each map has the same stability properties, as quantified through the restricted minimum singular value. These results can be viewed as new universality laws in high-dimensional stochastic geometry.
Universality laws for randomized dimension reduction have many applications in applied mathematics, signal processing, and statistics. They yield design principles for numerical linear algebra algorithms, for compressed sensing measurement ensembles, and for random linear codes. Furthermore, these results have implications for the performance of statistical estimation methods under a large class of random experimental designs
Universality laws for randomized dimension reduction, with applications
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space to facilitate its analysis. In the Euclidean setting, one fundamental technique for dimension reduction is to apply a random linear map to the data. This dimension reduction procedure succeeds when it preserves certain geometric features of the set.
The question is how large the embedding dimension must be to ensure that randomized dimension reduction succeeds with high probability.
This paper studies a natural family of randomized dimension reduction maps and a large class of data sets. It proves that there is a phase transition in the success probability of the dimension reduction map as the embedding dimension increases. For a given data set, the location of the phase transition is the same for all maps in this family. Furthermore, each map has the same stability properties, as quantified through the restricted minimum singular value. These results can be viewed as new universality laws in high-dimensional stochastic geometry.
Universality laws for randomized dimension reduction have many applications in applied mathematics, signal processing, and statistics. They yield design principles for numerical linear algebra algorithms, for compressed sensing measurement ensembles, and for random linear codes. Furthermore, these results have implications for the performance of statistical estimation methods under a large class of random experimental designs
From Review to Rating: Exploring Dependency Measures for Text Classification
Various text analysis techniques exist, which attempt to uncover unstructured
information from text. In this work, we explore using statistical dependence
measures for textual classification, representing text as word vectors. Student
satisfaction scores on a 3-point scale and their free text comments written
about university subjects are used as the dataset. We have compared two textual
representations: a frequency word representation and term frequency
relationship to word vectors, and found that word vectors provide a greater
accuracy. However, these word vectors have a large number of features which
aggravates the burden of computational complexity. Thus, we explored using a
non-linear dependency measure for feature selection by maximizing the
dependence between the text reviews and corresponding scores. Our quantitative
and qualitative analysis on a student satisfaction dataset shows that our
approach achieves comparable accuracy to the full feature vector, while being
an order of magnitude faster in testing. These text analysis and feature
reduction techniques can be used for other textual data applications such as
sentiment analysis.Comment: 8 page
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