Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Matzinger, Heinrich"'
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 iterative algorithm 'Concent' that actively eliminate this bias and re
Externí odkaz:
http://arxiv.org/abs/2201.00230
Johnson-Lindenstrauss guarantees certain topological structure is preserved under random projections when project high dimensional deterministic vectors to low dimensional vectors. In this work, we try to understand how random matrix affect norms of
Externí odkaz:
http://arxiv.org/abs/2112.00300
We study the spectrum reconstruction technique. As is known to all, eigenvalues play an important role in many research fields and are foundation to many practical techniques such like PCA(Principal Component Analysis). We believe that related algori
Externí odkaz:
http://arxiv.org/abs/2006.00152
Autor:
Chen, Jiangning, Dai, Zhibo, Duan, Juntao, Hu, Qianli, Li, Ruilin, Matzinger, Heinrich, Popescu, Ionel, Zhai, Haoyan
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 labels, we assign negative-oriented labels to the ambiguous training ex
Externí odkaz:
http://arxiv.org/abs/1906.03768
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 Bayes estimator to solve this problem. A correlation factor is introduced
Externí odkaz:
http://arxiv.org/abs/1905.06115
We define a new method to estimate centroid for text classification based on the symmetric KL-divergence between the distribution of words in training documents and their class centroids. Experiments on several standard data sets indicate that the ne
Externí odkaz:
http://arxiv.org/abs/1808.10261
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 a subunitary multiple of the number of observations. We test, compare
Externí odkaz:
http://arxiv.org/abs/1804.09472
Principal component analysis (PCA) is an indispensable tool in many learning tasks that finds the best linear representation for data. Classically, principal components of a dataset are interpreted as the directions that preserve most of its "energy"
Externí odkaz:
http://arxiv.org/abs/1803.04049
Principal component analysis is an important pattern recognition and dimensionality reduction tool in many applications. Principal components are computed as eigenvectors of a maximum likelihood covariance $\widehat{\Sigma}$ that approximates a popul
Externí odkaz:
http://arxiv.org/abs/1710.10124
We consider two independent binary i.i.d. random strings $X$ and $Y$ of equal length $n$ and the optimal alignments according to a symmetric scoring functions only. We decompose the space of scoring functions into five components. Two of these compon
Externí odkaz:
http://arxiv.org/abs/1703.05788