Influential features PCA for high dimensional clustering
نویسندگان
چکیده
منابع مشابه
Influential Features Pca for High Dimensional Clustering
We consider a clustering problem where we observe feature vectors Xi ∈ R, i = 1, 2, . . . , n, from K possible classes. The class labels are unknown and the main interest is to estimate them. We are primarily interested in the modern regime of p n, where classical clustering methods face challenges. We propose Influential Features PCA (IF-PCA) as a new clustering procedure. In IF-PCA, we select...
متن کاملDiscussion of Influential Features Pca for High Dimensional Clustering
We commend Jin and Wang on a very interesting paper introducing a novel approach to feature selection within clustering and a detailed analysis of its clustering performance under a Gaussian mixture model. I shall divide my discussion into several parts: (i) prior work on feature selection and clustering; (ii) theoretical aspects; (iii) practical aspects; and finally (iv) some questions and dir...
متن کاملImportant Features PCA for high dimensional clustering
We consider a clustering problem where we observe feature vectors Xi ∈ R, i = 1, 2, . . . , n, from K possible classes. The class labels are unknown and the main interest is to estimate them. We are primarily interested in the modern regime of p n, where classical clustering methods face challenges. We propose Important Features PCA (IF-PCA) as a new clustering procedure. In IFPCA, we select a ...
متن کاملDiscussion of “ Influential Feature Pca for High Dimensional Clustering ”
We would like to congratulate the authors for an interesting paper and a novel proposal for clustering high-dimensional Gaussian mixtures with a diagonal covariance matrix. The proposed two-stage procedure first selects features based on the Kolmogorov-Smirnov statistics and then applies a spectral clustering method to the post-selected data. A rigorous theoretical analysis for the clustering e...
متن کاملDiscussion of “ Influential Features Pca for High Dimensional Clustering ” , by J . Jin And
where z : {1, . . . , n} → {1, . . . ,K} is an unknown assignment of the observations to K classes, μ1, . . . , μK are unknown vectors in Rp, and Zi ∈ Rp are i.i.d. normal vectors with mean 0 and covariance matrix σIp. Here Ip is the p× p identity matrix. In [JW], the covariance matrix of Z1 is diagonal, with the diagonal elements bounded from below and from above by constants red that are inde...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2016
ISSN: 0090-5364
DOI: 10.1214/15-aos1423