R topics documented: glasso............................................ 1 glassopath................ more R topics documented: glasso............................................ 1 glassopath.......................................... 3
Regularization in linear regression and classildots cation is viewed as a two–stage process. Firs... more Regularization in linear regression and classildots cation is viewed as a two–stage process. First a set of candidate models is deldots ned by a path through the space of joint parameter values, and then a point on this path is chosen to be the ldots nal model. Various pathldots nding strategies for the ldots rst stage of this process are examined, based on the notion of generalized gradient descent. Several of these strategies are seen to produce paths that closely corre- spond to those induced by commonly used penalization methods. Others give rise to new regularization techniques that are shown to be advantageous in some situations. In all cases, the gradient descent pathldots nding paradigm can be readily generalized to include the use of a wide variety of loss criteria, leading to robust methods for regression and classildots cation, as well as to apply user deldots ned constraints on the parameter values.
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2004
SummaryA new procedure is proposed for clustering attribute value data. When used in conjunction ... more SummaryA new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.
R topics documented: glasso............................................ 1 glassopath................ more R topics documented: glasso............................................ 1 glassopath.......................................... 3
Regularization in linear regression and classildots cation is viewed as a two–stage process. Firs... more Regularization in linear regression and classildots cation is viewed as a two–stage process. First a set of candidate models is deldots ned by a path through the space of joint parameter values, and then a point on this path is chosen to be the ldots nal model. Various pathldots nding strategies for the ldots rst stage of this process are examined, based on the notion of generalized gradient descent. Several of these strategies are seen to produce paths that closely corre- spond to those induced by commonly used penalization methods. Others give rise to new regularization techniques that are shown to be advantageous in some situations. In all cases, the gradient descent pathldots nding paradigm can be readily generalized to include the use of a wide variety of loss criteria, leading to robust methods for regression and classildots cation, as well as to apply user deldots ned constraints on the parameter values.
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2004
SummaryA new procedure is proposed for clustering attribute value data. When used in conjunction ... more SummaryA new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.
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Papers by Jerome Friedman