Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data

<p>Abstract</p> <p>Background</p> <p>More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA) and its modification methods for the classification of cancer based on gene expression data.</p> <p>Methods</p> <p>The classification performance of linear discriminant analysis (LDA) and its modification methods was evaluated by applying these methods to six public cancer gene expression datasets. These methods included linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), shrinkage centroid regularized discriminant analysis (SCRDA), shrinkage linear discriminant analysis (SLDA) and shrinkage diagonal discriminant analysis (SDDA). The procedures were performed by software R 2.80.</p> <p>Results</p> <p>PAM picked out fewer feature genes than other methods from most datasets except from Brain dataset. For the two methods of shrinkage discriminant analysis, SLDA selected more genes than SDDA from most datasets except from 2-class lung cancer dataset. When comparing SLDA with SCRDA, SLDA selected more genes than SCRDA from 2-class lung cancer, SRBCT and Brain dataset, the result was opposite for the rest datasets. The average test error of LDA modification methods was lower than LDA method.</p> <p>Conclusions</p> <p>The classification performance of LDA modification methods was superior to that of traditional LDA with respect to the average error and there was no significant difference between theses modification methods.</p

Implications of the latest XENON100 and cosmic ray antiproton data for isospin violating dark matter

In the scenario of isospin-violating dark matter (IVDM), the dark matter (DM) spin-independent couplings to protons and to neutrons are allowed to be different, which has been considered to relax the tensions between the results of DAMA, CoGeNT and XENON experiments. We explore the allowed values of DM-nucleon couplings favored and excluded by the current experiments under the assumption of IVDM. We find that the recently updated XENON100 result excludes the main part of the overlapping signal region between DAMA and CoGeNT. The mass of the IVDM is restricted to a narrow range near 7.5-8 GeV. We also show that the possible tensions between some experiments such as that between DAMA and SIMPLE are unlikely to be affected by isospin violating interactions. In an effective operator approach, we investigate conservative upper bounds on the DM-quark couplings required by the IVDM scenario from the cosmic ray antiproton fluxes measured recently by BESS-Polar II and PAMELA, and that from the relic density. The results show that the relatively large couplings favored by DAMA and CoGeNT are tightly constrained, if the operators contribute to velocity-independent annihilation cross sections. For thermal relic DM, the upper bounds from the relic density can also be stringent.Comment: 26 pages, 8 figs, match the published versio

A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution, resulting in the underlined bias of attributes co-occurrence. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. Rooting from it, practical strategies are devised to efficiently decouple attributes, which substantially improve the baseline and establish state-of-the-art performance on realistic datasets like PETAzs and RAPzs. Code is released on https://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition.Comment: Accepted in IJCAI2