Deep Neural Networks for High Dimension, Low Sample Size Data

By Bo Liu, Ying Wei, Yu Zhang, Qiang Yang. 2017

222 citations recorded by the source corpus.

Deep neural networks (DNN) have achieved breakthroughs in applications with large sample size. However, when facing high dimension, low sample size (HDLSS) data, such as the phenotype prediction problem using genetic data in bioinformatics, DNN suffers from overfitting and high-variance gradients. In this paper, we propose a DNN model tailored for the HDLSS data, named Deep Neural Pursuit (DNP). DNP selects a subset of high dimensional features for the alleviation of overfitting and takes the average over multiple dropouts to calculate gradients with low variance. As the first DNN method applied on the HDLSS data, DNP enjoys the advantages of the high nonlinearity, the robustness to high dimensionality, the capability of learning from a small number of samples, the stability in feature selection, and the end-to-end training. We demonstrate these advantages of DNP via empirical results on both synthetic and real-world biological datasets.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

A Geometric Solution to Fair Representations (2020, background): “While NNs are critical for, e.g., image recognition [8], other methods perform better for small data [25], especially when the number of dimensions is high and the sample size low [19].”
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification (2018, background): “A recent work [18] performed the joint training of greedy feature selection and a deep classiﬁer; but their model was designed for bioinformatics data (attributed vectors) and it was unclear how a similar model can be applied to raw images.”