Discussion of : Treelets — an Adaptive Multi - Scale Basis for Sparse Unordered Data
نویسنده
چکیده
1. Unsupervised learning. The authors’ emphasis is on the method as a useful way of representing data analogous to a wavelet representation where X = X(t) with t genuinely identified with a point on the line and observation at p time points, but where the time points have been permuted. As such, this can be viewed as a clustering method which, from their examples, gives very reasonable answers. However, to make more general theoretical statements and to permit comparison to other methods, they necessarily introduce the model
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Treelets—an Adaptive Multi-scale Basis for Sparse Unordered Data by Ann
In many modern applications, including analysis of gene expression and text documents, the data are noisy, high-dimensional, and unordered—with no particular meaning to the given order of the variables. Yet, successful learning is often possible due to sparsity: the fact that the data are typically redundant with underlying structures that can be represented by only a few features. In this pape...
متن کاملDiscussion Of: Treelets-an Adaptive Multi-scale Basis for Sparse Unordered Data.
We would like to congratulate Lee, Nadler and Wasserman on their contribution to clustering and data reduction methods for high p and low n situations. A composite of clustering and traditional principal components analysis, treelets is an innovative method for multi-resolution analysis of unordered data. It is an improvement over traditional PCA and an important contribution to clustering meth...
متن کاملDiscussion Of: Treelets—an Adaptive Multi-scale Basis for Sparse Unordered Data by Nicolai Meinshausen
We congratulate Lee, Nadler and Wasserman (henceforth LNW) on a very interesting paper on new methodology and supporting theory. Treelets seem to tackle two important problems of modern data analysis at once. For datasets with many variables, treelets give powerful predictions even if variables are highly correlated and redundant. Maybe more importantly, interpretation of the results is intuiti...
متن کاملar X iv : 0 70 7 . 04 81 v 2 [ st at . M E ] 3 1 A ug 2 00 7 Treelets — An Adaptive Multi - Scale Basis for Sparse Unordered Data
In many modern applications, including analysis of gene expression and text documents, the data are noisy, high-dimensional, and unordered — with no particular meaning to the given order of the variables. Yet, successful learning is often possible due to sparsity: the fact that the data are typically redundant with underlying structures that can be represented by only a few features. In this pa...
متن کاملar X iv : 0 70 7 . 04 81 v 1 [ st at . M E ] 3 J ul 2 00 7 Treelets — An Adaptive Multi - Scale Basis for Sparse Unordered Data
In many modern applications, including analysis of gene expression and text documents, the data are noisy, high-dimensional, and unordered — with no particular meaning to the given order of the variables. Yet, successful learning is often possible due to sparsity; the fact that the data are typically redundant with underlying structures that can be represented by only a few features. In this pa...
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