2 citations · 4 across the 8 of their papers we have counts for
9 papers
Sparse Positive-Definite Estimation for Covariance Matrices with Repeated Measurements
Sunpeng Duan, Guo Yu, Juntao Duan +1
Repeated measurements are common in many fields, where random variables are observed repeatedly across different subjects. Such data have an underlying hierarchical structure, and…
LoCoV: low dimension covariance voting algorithm for portfolio optimization
JunTao Duan, Ionel Popescu
Minimum-variance portfolio optimizations rely on accurate covariance estimator to obtain optimal portfolios. However, it usually suffers from large error from sample covariance mat…
Recover the spectrum of covariance matrix: a non-asymptotic iterative method
Juntao Duan, Ionel Popescu, Heinrich Matzinger
It is well known the sample covariance has a consistent bias in the spectrum, for example spectrum of Wishart matrix follows the Marchenko-Pastur law. We in this work introduce an…
Invariance principle of random projection for the norm
Juntao Duan, Ionel Popescu, Heinrich Matzinger
Johnson-Lindenstrauss guarantees certain topological structure is preserved under random projections when project high dimensional deterministic vectors to low dimensional vectors.…
A product-CLT and its application in invariance principle of random projection
JunTao Duan, Ionel Popescu, Fan Zhou
Johnson-Lindenstrauss lemma states random projections can be used as a topology preserving embedding technique for fixed vectors. In this paper, we try to understand how random pro…
A cost-reducing partial labeling estimator in text classification problem
Jiangning Chen, Zhibo Dai, Juntao Duan +5
We propose a new approach to address the text classification problems when learning with partial labels is beneficial. Instead of offering each training sample a set of candidate l…
Naive Bayes with Correlation Factor for Text Classification Problem
Jiangning Chen, Zhibo Dai, Juntao Duan +2
Naive Bayes estimator is widely used in text classification problems. However, it doesn't perform well with small-size training dataset. We propose a new method based on Naive Baye…
Recovery of spectrum from estimated covariance matrices and statistical kernels for machine learning and big data
Saba Amsalu, Juntao Duan, Heinrich Matzinger +1
In this paper we propose two schemes for the recovery of the spectrum of a covariance matrix from the empirical covariance matrix, in the case where the dimension of the matrix is…
Non-normal limiting distribution for optimal alignment scores of strings in binary alphabets
Jun Tao Duan, Heinrich Matzinger, Ionel Popescu
We consider two independent binary i.i.d. random strings and of equal length and the optimal alignments according to a symmetric scoring functions only. We decompose th…