4,592 research outputs found
Weighted k-Nearest-Neighbor Techniques and Ordinal Classification
In the field of statistical discrimination k-nearest neighbor classification is a well-known, easy and successful method. In this paper we present an extended version of this technique, where the distances of the nearest neighbors can be taken into account. In this sense there is a close connection to LOESS, a local regression technique. In addition we show possibilities to use nearest neighbor for classification in the case of an ordinal class structure. Empirical studies show the advantages of the new techniques
Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
A number of variable selection methods have been proposed involving nonconvex
penalty functions. These methods, which include the smoothly clipped absolute
deviation (SCAD) penalty and the minimax concave penalty (MCP), have been
demonstrated to have attractive theoretical properties, but model fitting is
not a straightforward task, and the resulting solutions may be unstable. Here,
we demonstrate the potential of coordinate descent algorithms for fitting these
models, establishing theoretical convergence properties and demonstrating that
they are significantly faster than competing approaches. In addition, we
demonstrate the utility of convexity diagnostics to determine regions of the
parameter space in which the objective function is locally convex, even though
the penalty is not. Our simulation study and data examples indicate that
nonconvex penalties like MCP and SCAD are worthwhile alternatives to the lasso
in many applications. In particular, our numerical results suggest that MCP is
the preferred approach among the three methods.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS388 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
PLS dimension reduction for classification of microarray data
PLS dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, PLS is compared with some of the best state-of-the-art classification methods. In addition, a simple procedure to choose the number of components is suggested. The connection between PLS dimension reduction and gene selection is examined and a property of the first PLS component for binary classification is proven. PLS can also be used as a visualization tool for high-dimensional data in the classification framework. The whole study is based on 9 real microarray cancer data sets
Sparse regulatory networks
In many organisms the expression levels of each gene are controlled by the
activation levels of known "Transcription Factors" (TF). A problem of
considerable interest is that of estimating the "Transcription Regulation
Networks" (TRN) relating the TFs and genes. While the expression levels of
genes can be observed, the activation levels of the corresponding TFs are
usually unknown, greatly increasing the difficulty of the problem. Based on
previous experimental work, it is often the case that partial information about
the TRN is available. For example, certain TFs may be known to regulate a given
gene or in other cases a connection may be predicted with a certain
probability. In general, the biology of the problem indicates there will be
very few connections between TFs and genes. Several methods have been proposed
for estimating TRNs. However, they all suffer from problems such as unrealistic
assumptions about prior knowledge of the network structure or computational
limitations. We propose a new approach that can directly utilize prior
information about the network structure in conjunction with observed gene
expression data to estimate the TRN. Our approach uses penalties on the
network to ensure a sparse structure. This has the advantage of being
computationally efficient as well as making many fewer assumptions about the
network structure. We use our methodology to construct the TRN for E. coli and
show that the estimate is biologically sensible and compares favorably with
previous estimates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS350 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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