RECORD
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Computer Vision and Pattern Recognition · Submitted 7 Jan 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The promising experimental results validate the superiority of the proposed method.
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The abstract, sentence by sentence
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix. However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear. To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning. More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work. Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space. Extensive experiments are conducted on image, audio, text, and biological data. The promising experimental results validate the superiority of the proposed method.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s2 | These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix. | background 0.98 | 0.05 | 0.02 | asserted 1.00 |
| s3 | However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear. | gap 0.83 | 0.06 | 0.03 | asserted 0.58 |
| s4 | To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning. | contribution 0.94 | 0.88 | 0.08 | asserted 0.99 |
| s5 | More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work. | method 0.98 | 0.89 | 0.02 | asserted 0.99 |
| s6 | Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space. | method 0.81 | 0.93 | 0.04 | asserted 0.96 |
| s7 | Extensive experiments are conducted on image, audio, text, and biological data. | method 0.83 | 0.93 | 0.03 | asserted 0.87 |
| s8 | The promising experimental results validate the superiority of the proposed method. | result 0.53 | 0.88 | 0.84 | asserted 0.90 |
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The paper
Original abstract
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix. However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear. To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning. More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work. Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space. Extensive experiments are conducted on image, audio, text, and biological data. The promising experimental results validate the superiority of the proposed method.
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How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 2 sentences without a confident label
Uses the method or tool of 1 paper
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To be more specific, we use the Keras tool [ref] with the pre-trained VGG-19 model [ref] .
Very Deep Convolutional Networks for Large-Scale Image Recognition · Data Description · 1.00
Cites as background 1 paper
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Feature selection methods can be categorized into groups based on different criteria summarized below; refer to [ref] for a detailed survey on feature selection. * Label Availability.
Feature Selection: A Data Perspective · Introduction · 0.99
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