Multimodal semantic analysis with regularized semantic autoencoder

2021 ◽  
pp. 1-9
Author(s):  
Shaily Malik ◽  
Poonam Bansal
Keyword(s):  
Semantic Analysis ◽  
Feature Space ◽  
Machine Learning Algorithms ◽  
Real World Data ◽  
Common Space ◽  
Locality Preservation ◽  
Space Transformation ◽  
Image Query ◽  
Text Query ◽  
Low Dimensional

The real-world data is multimodal and to classify them by machine learning algorithms, features of both modalities must be transformed into common latent space. The high dimensional common space transformation of features lose their locality information and susceptible to noise. This research article has dealt with this issue of a semantic autoencoder and presents a novel algorithm with distinct mapped features with locality preservation into a commonly hidden space. We call it discriminative regularized semantic autoencoder (DRSAE). It maintains the low dimensional features in the manifold to manage the inter and intra-modality of the data. The data has multi labels, and these are transformed into an aware feature space. Conditional Principal label space transformation (CPLST) is used for it. With the two-fold proposed algorithm, we achieve a significant improvement in text retrieval form image query and image retrieval from the text query.

Hypomimia Recognition in Parkinson’s Disease With Semantic Features

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-20
Author(s):  
Ge Su ◽  
Bo Lin ◽  
Wei Luo ◽  
Jianwei Yin ◽  
Shuiguang Deng ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Spatial Data ◽  
Semantic Analysis ◽  
Neurodegenerative Disorder ◽  
Machine Learning Algorithms ◽  
Semantic Feature ◽  
Semantic Features ◽  
Feature Maps ◽  
Real World Data

Parkinson’s disease is the second most common neurodegenerative disorder, commonly affecting elderly people over the age of 65. As the cardinal manifestation, hypomimia, referred to as impairments in normal facial expressions, stays covert. Even some experienced doctors may miss these subtle changes, especially in a mild stage of this disease. The existing methods for hypomimia recognition are mainly dominated by statistical variable-based methods with the help of traditional machine learning algorithms. Despite the success of recognizing hypomimia, they show a limited accuracy and lack the capability of performing semantic analysis. Therefore, developing a computer-aided diagnostic method for semantically recognizing hypomimia is appealing. In this article, we propose a Semantic Feature based Hypomimia Recognition network , named SFHR-NET , to recognize hypomimia based on facial videos. First, a Semantic Feature Classifier (SF-C) is proposed to adaptively adjust feature maps salient to hypomimia, which leads the encoder and classifier to focus more on areas of hypomimia-interest. In SF-C, the progressive confidence strategy (PCS) ensures more reliable semantic features. Then, a two-stream framework is introduced to fuse the spatial data stream and temporal optical stream, which allows the encoder to semantically and progressively characterize the rigid process of hypomimia. Finally, to improve the interpretability of the model, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to generate attention maps that cast our engineered features into hypomimia-interest regions. These highlighted regions provide visual explanations for decisions of our network. Experimental results based on real-world data demonstrate the effectiveness of our method in detecting hypomimia.

scholarly journals Modality-Dependent Cross-Modal Retrieval Based on Graph Regularization

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Guanhua Wang ◽  
Hua Ji ◽  
Dexin Kong ◽  
Na Zhang
Keyword(s):  
Semantic Information ◽  
Feature Space ◽  
Heterogeneous Data ◽  
Query Image ◽  
Graph Regularization ◽  
Semantic Correlation ◽  
Image Query ◽  
Class Labels ◽  
Text Query ◽  
Original Feature

Nowadays, the heterogeneity gap of different modalities is the key problem for cross-modal retrieval. In order to overcome heterogeneity gaps, potential correlations of different modalities need to be mined. At the same time, the semantic information of class labels is used to reduce the semantic gaps between different modalities data and realize the interdependence and interoperability of heterogeneous data. In order to fully exploit the potential correlation of different modalities, we propose a cross-modal retrieval framework based on graph regularization and modality dependence (GRMD). Firstly, considering the potential feature correlation and semantic correlation, different projection matrices are learned for different retrieval tasks, such as image query text (I2T) or text query image (T2I). Secondly, utilizing the internal structure of original feature space constructs an adjacent graph with semantic information constraints which can make different labels of heterogeneous data closer to the corresponding semantic information. The experimental results on three widely used datasets demonstrate the effectiveness of our method.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals An Incremental Classification Algorithm for Mining Data with Feature Space Heterogeneity

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yu Wang
Keyword(s):  
Feature Space ◽  
Classification Problem ◽  
Classification Algorithm ◽  
Data Sets ◽  
Real World Data ◽  
Supervised Clustering ◽  
Online Classification ◽  
Efficiency And Effectiveness ◽  
Feature Relevance ◽  
Incremental Classification

Feature space heterogeneity often exists in many real world data sets so that some features are of different importance for classification over different subsets. Moreover, the pattern of feature space heterogeneity might dynamically change over time as more and more data are accumulated. In this paper, we develop an incremental classification algorithm, Supervised Clustering for Classification with Feature Space Heterogeneity (SCCFSH), to address this problem. In our approach, supervised clustering is implemented to obtain a number of clusters such that samples in each cluster are from the same class. After the removal of outliers, relevance of features in each cluster is calculated based on their variations in this cluster. The feature relevance is incorporated into distance calculation for classification. The main advantage of SCCFSH lies in the fact that it is capable of solving a classification problem with feature space heterogeneity in an incremental way, which is favorable for online classification tasks with continuously changing data. Experimental results on a series of data sets and application to a database marketing problem show the efficiency and effectiveness of the proposed approach.

Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahmet Mert ◽  
Hasan Huseyin Celik
Keyword(s):  
Emotion Recognition ◽  
Feature Vector ◽  
Dimensional Space ◽  
Computational Cost ◽  
Feature Space ◽  
Maximum Energy ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Time Frequency ◽  
Synchrosqueezing Transform

Abstract The feasibility of using time–frequency (TF) ridges estimation is investigated on multi-channel electroencephalogram (EEG) signals for emotional recognition. Without decreasing accuracy rate of the valence/arousal recognition, the informative component extraction with low computational cost will be examined using multivariate ridge estimation. The advanced TF representation technique called multivariate synchrosqueezing transform (MSST) is used to obtain well-localized components of multi-channel EEG signals. Maximum-energy components in the 2D TF distribution are determined using TF-ridges estimation to extract instantaneous frequency and instantaneous amplitude, respectively. The statistical values of the estimated ridges are used as a feature vector to the inputs of machine learning algorithms. Thus, component information in multi-channel EEG signals can be captured and compressed into low dimensional space for emotion recognition. Mean and variance values of the five maximum-energy ridges in the MSST based TF distribution are adopted as feature vector. Properties of five TF-ridges in frequency and energy plane (e.g., mean frequency, frequency deviation, mean energy, and energy deviation over time) are computed to obtain 20-dimensional feature space. The proposed method is performed on the DEAP emotional EEG recordings for benchmarking, and the recognition rates are yielded up to 71.55, and 70.02% for high/low arousal, and high/low valence, respectively.

scholarly journals A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms

2019 ◽  
Vol 29 (07) ◽  
pp. 1850058 ◽  
Author(s):  
Juan M. Górriz ◽  
Javier Ramírez ◽  
F. Segovia ◽  
Francisco J. Martínez ◽  
Meng-Chuan Lai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Structure ◽  
Feature Space ◽  
Classification Problem ◽  
Small Sample ◽  
Biological Sex ◽  
Machine Learning Approach ◽  
Learning Machine ◽  
Small Sample Sizes ◽  
Low Dimensional

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.

scholarly journals Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

2021 ◽  
Author(s):  
Rogini Runghen ◽  
Daniel B Stouffer ◽  
Giulio Valentino Dalla Riva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Link Prediction ◽  
Graph Embedding ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Large Networks ◽  
Data Set ◽  
Learning Techniques ◽  
Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

scholarly journals A Novel Density-based Technique for Outlier Detection of High Dimensional Data Utilizing Full Feature Space

2021 ◽  
Vol 50 (1) ◽  
pp. 138-152
Author(s):  
Mujeeb Ur Rehman ◽  
Dost Muhammad Khan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
High Dimensional Data ◽  
Research Work ◽  
Feature Space ◽  
High Dimensional ◽  
Data Set ◽  
Data Points ◽  
Low Dimensional ◽  
Intrinsic Feature

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

Sign in / Sign up

Export Citation Format

Share Document