156,588 research outputs found
On Identity Tests for High Dimensional Data Using RMT
In this work, we redefined two important statistics, the CLRT test (Bai
et.al., Ann. Stat. 37 (2009) 3822-3840) and the LW test (Ledoit and Wolf, Ann.
Stat. 30 (2002) 1081-1102) on identity tests for high dimensional data using
random matrix theories. Compared with existing CLRT and LW tests, the new tests
can accommodate data which has unknown means and non-Gaussian distributions.
Simulations demonstrate that the new tests have good properties in terms of
size and power. What is more, even for Gaussian data, our new tests perform
favorably in comparison to existing tests. Finally, we find the CLRT is more
sensitive to eigenvalues less than 1 while the LW test has more advantages in
relation to detecting eigenvalues larger than 1.Comment: 16 pages, 2 figures, 3 tables, To be published in the Journal of
Multivariate Analysi
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
The Grone-Merris Conjecture
In spectral graph theory, Grone and Merris conjecture that the spectrum of
the Laplacian matrix of a finite graph is majorized by the conjugate degree
sequence of this graph. We give a complete proof for this conjecture.Comment: The paper is accepted by Transactions of the American Mathematical
Societ
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