Semi-supervised sparse feature selection based on low-dimensional space Hessian regularization considering feature manifolds

In order to solve the problem of face recognition, the method of feature extraction and feature selection is presented in this paper. First using Gabor filters and face image as the convolution Operator to extract the Gabor feature vector of the image and also to uniform sampling; then using the PCA + LDA method to reduce the dimension for high-dimensional Gabor feature vector; Finally, using the nearest neighbor classifier to discriminate and determine the identity of a face image. The result I get is that the sampled Gabor feature in high-dimensional space can be projected onto low-dimensional space though the method of feature selection and compression. The new and original in this paper is that the method of PCA + LDA overcomes the problem of the spread matrix singular in the class and matrix too large which is brought by directly use the LDA.

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Unsupervised feature selection via graph matrix learning and the low-dimensional space learning for classification

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2019.103283 ◽

2020 ◽

Vol 87 ◽

pp. 103283 ◽

Cited By ~ 1

Author(s):

Xiaohong Han ◽

Ping Liu ◽

Li Wang ◽

Dengao Li

Keyword(s):

Feature Selection ◽

Dimensional Space ◽

Unsupervised Feature Selection ◽

Low Dimensional

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Distributed Collaborative Feature Selection Based on Intermediate Representation

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/575 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xiucai Ye ◽

Hongmin Li ◽

Akira Imakura ◽

Tetsuya Sakurai

Keyword(s):

Feature Selection ◽

Dimensional Space ◽

Original Data ◽

Intermediate Representation ◽

Selection Methods ◽

Dimensionality Reduction Technique ◽

Selection For ◽

Real World Datasets ◽

Low Dimensional ◽

Row Sparsity

Feature selection is an efficient dimensionality reduction technique for artificial intelligence and machine learning. Many feature selection methods learn the data structure to select the most discriminative features for distinguishing different classes. However, the data is sometimes distributed in multiple parties and sharing the original data is difficult due to the privacy requirement. As a result, the data in one party may be lack of useful information to learn the most discriminative features. In this paper, we propose a novel distributed method which allows collaborative feature selection for multiple parties without revealing their original data. In the proposed method, each party finds the intermediate representations from the original data, and shares the intermediate representations for collaborative feature selection. Based on the shared intermediate representations, the original data from multiple parties are transformed to the same low dimensional space. The feature ranking of the original data is learned by imposing row sparsity on the transformation matrix simultaneously. Experimental results on real-world datasets demonstrate the effectiveness of the proposed method.

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Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Brain Structure and Function ◽

10.1007/s00429-021-02418-1 ◽

2021 ◽

Author(s):

Patrick Friedrich ◽

Kaustubh R. Patil ◽

Lisa N. Mochalski ◽

Xuan Li ◽

Julia A. Camilleri ◽

...

Keyword(s):

Feature Selection ◽

Right Hemisphere ◽

Dimensional Space ◽

High Dimensional ◽

Hemispheric Differences ◽

Dimensional Representation ◽

Left And Right ◽

Low Dimensional ◽

The Right ◽

And Function

AbstractHemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

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A Generative-Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space

NeuroImage ◽

10.1016/j.neuroimage.2021.118200 ◽

2021 ◽

pp. 118200

Author(s):

Sayan Ghosal ◽

Qiang Chen ◽

Giulio Pergola ◽

Aaron L. Goldman ◽

William Ulrich ◽

...

Keyword(s):

Dimensional Space ◽

Low Dimensional

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An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification

Sensors ◽

10.3390/s21113627 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3627

Author(s):

Bo Jin ◽

Chunling Fu ◽

Yong Jin ◽

Wei Yang ◽

Shengbin Li ◽

...

Keyword(s):

Feature Selection ◽

Local Structure ◽

Gene Selection ◽

Dimensional Space ◽

Original Data ◽

Global Structure ◽

Biological Data ◽

Special Treatment ◽

Selection Scheme ◽

Unsupervised Feature Selection

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

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Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

Sensors ◽

10.3390/s19204454 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4454 ◽

Cited By ~ 2

Author(s):

Marek Piorecky ◽

Vlastimil Koudelka ◽

Jan Strobl ◽

Martin Brunovsky ◽

Vladimir Krajca

Keyword(s):

Data Structure ◽

Spatial Clustering ◽

Dimensional Space ◽

Head Movements ◽

Nonlinear Dimensionality Reduction ◽

Space Resolution ◽

Additional Information ◽

Nonlinear Dimension ◽

The Time Domain ◽

Low Dimensional

Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI.

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Group sparse feature selection on local learning based clustering

Neurocomputing ◽

10.1016/j.neucom.2015.07.045 ◽

2016 ◽

Vol 171 ◽

pp. 1118-1130 ◽

Cited By ~ 12

Author(s):

Yue Wu ◽

Can Wang ◽

Jiajun Bu ◽

Chun Chen

Keyword(s):

Feature Selection ◽

Local Learning ◽

Sparse Feature Selection

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Optimizing the structure and movement of a robotic bat with biological kinematic synergies

The International Journal of Robotics Research ◽

10.1177/0278364918804654 ◽

2018 ◽

Vol 37 (10) ◽

pp. 1233-1252 ◽

Cited By ~ 2

Author(s):

Jonathan Hoff ◽

Alireza Ramezani ◽

Soon-Jo Chung ◽

Seth Hutchinson

Keyword(s):

Principal Component Analysis ◽

Topological Structure ◽

Degrees Of Freedom ◽

Dimensional Space ◽

Principal Component ◽

Biologically Inspired ◽

Wing Kinematics ◽

Wing Motion ◽

Kinematic Synergies ◽

Low Dimensional

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.

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