Dynamic Feature Space Research Articles

Online learning for data streams with bi-dynamic distributions

Data streams, as an important pattern of big data, require online real-time processing because instances arrive one by one and are fleeting. Existing online learning methods make distinctive assumptions, such as a fixed feature space, a varying feature space that follows specific patterns, and a fixed data distribution. However, data streams generated from real-world scenarios typically have both randomly changing feature spaces and data distributions, making existing methods inappropriate for practical applications. To fill this gap, this study proposes a novel Online Learning for Data Streams with Bi-dynamic Distributions (OLBD) algorithm. OLBD has a two-fold main idea: 1) it overcomes random changes in the feature space by building a mapping matrix to space transform and projects the original instances onto the global feature space; 2) it handles dynamic data distributions by constraining prior knowledge and transferring established mapping relationships to new distributions. To evaluate OLBD, we compared it with related state-of-the-art algorithms. First, we use 13 datasets to simulate three scenarios of dynamic feature space, namely trapezoidal, feature evolvable, and capricious data streams. Second, we simulated the data streams with dynamic data distributions using eight real and four generated datasets. We then conducted ablation studies on the parameter α. Finally, we analyzed data streams with bi-dynamic data distributions under different feature missing ratios and verified the generalization. The results show that OLBD significantly outperforms its rivals. Additionally, a practical case study on movie review classification was conducted to illustrate the effectiveness of OLBD in real-world scenarios.

TF-IDF Vectorization and Clustering for Extractive Text Summarization

Extractive document summarization is a vital technique for condensing large volumes of text while retaining key information. This research introduces a dynamic feature space mapping approach to enhance extractive document summarization, aiming to succinctly encapsulate key information from extensive text volumes. The proposed method involves extracting various document properties like term frequency, sentence length, and position to comprehensively describe content. By employing a mapping function, these features are projected into a dynamic feature space, enhancing summarization efficiency and feature clarity. Clustering similar phrases in this space facilitates easier sentence grouping, aiding summary creation. Leveraging TF-IDF vectorization, the most representative phrases are chosen from each cluster based on importance and diversity. This process culminates in generating a high-quality document summary quickly and systematically. The dynamic mapping method streamlines sentence grouping, systematically capturing essential document attributes. This approach addresses challenges in extractive summarization, contributing significantly to automated text summarization. Its applicability spans domains requiring rapid extraction of information from vast textual data.

Data stream classification in dynamic feature space using feature mapping

Data stream classification in dynamic feature space using feature mapping

An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR

Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful and challenging to distinguish the unseen target classes, i.e., open set recognition (OSR) problem, which is an urgent problem for the practical SAR ATR. The key solution of OSR is to effectively establish the exclusiveness of feature distribution of known classes. In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes. Through meta-learning tasks, the proposed method learns to construct a feature space of the dynamic-assigned known classes. This feature space is required by the tasks to reject all other classes not belonging to the known classes. At the same time, the proposed entropy-awareness loss helps the model to enhance the feature space with effective and robust discrimination between the known and unknown classes. Therefore, our method can construct a dynamic feature space with discrimination between the known and unknown classes to simultaneously classify the dynamic-assigned known classes and reject the unknown classes. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown the effectiveness of our method for SAR OSR.

Dynamic hidden feature space detection of noisy image set by weight binarization

Dynamic hidden feature space detection of noisy image set by weight binarization

Context-Aware Recommendations Based on Deep Learning Frameworks

In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in various ways for multiple purposes, such as rating prediction, generating top-k recommendations, and classification of users’ feedback. Specifically, based on the suggested framework, we propose three deep context-aware recommendation models based on explicit, unstructured, and structured latent representations of contextual data derived from various contextual dimensions (e.g., time, location, user activity). Offline evaluation on three context-aware datasets confirms that our proposed deep context-aware models surpass state-of-the-art context-aware methods. We also show that utilizing structured latent contexts in the proposed deep recommendation framework achieves significantly better performance than the other context-aware models on all datasets.

Self-blast state detection of glass insulators based on stochastic configuration networks and a feedback transfer learning mechanism

The self-blast state of a glass insulator directly affects the safety and reliability of transmission lines. To address the insufficient generalization ability of existing detection methods for insulator self-blast states and the drawbacks of deep neural network structures, the theories of transfer learning and closed-loop control are drawn upon to provide an intelligent detection method for the self-blast states of glass insulators. The method proposed in this paper is based on stochastic configuration networks and a feedback transfer learning mechanism. First, to reduce the redundancy of convolutional kernels in the channel extent, the interleaved group convolution strategy is employed to reconstruct the convolutional layers of the Inception network. Second, in view of the different feature applicabilities of different glass insulator images and based on the adaptive convolution module groups, the data structure of the dynamic feature space of insulator images is built with a certain mapping relationship from global to local. Then, the discriminative measure index is used to evaluate the discriminative information of the feature space to enhance the interpretability of the compact feature spance. Third, the fully connected feature vector of the compact feature space is sent to stochastic configuration networks (SCNs), which have universal approximation property to establish the classification criteria of the self-blast states of insulator images with generalization ability. Finally, an imitation of human thinking patterns is employed that exhibits repeated deliberation and comparison. Consequently, based on generalized error and entropy theories, the evaluation index of the objective function is established to evaluate the uncertain detection results of the self-blast states of glass insulator images in real time. Then, the dynamic transfer learning mechanism is constructed based on the constraint of the measurement index of uncertain detection results to realize self-optimizing regulation of the feature space that exhibits multihierarchy and discrimination and reconstructed classification criteria. The experimental results show that compared with other algorithms, the proposed method enhances the generalization ability and detection accuracy of the model.

Extractive Document Summarization Based on Dynamic Feature Space Mapping

The exponential growth of the Web documents has constituted the need for automatic document summarization. In this context, extractive document summarization, i.e., that task of extracting the most relevant information, removing redundancy and presenting the remained data in a coherent and cohesive structure, is a challenging task. In this article, we propose a novel intelligent approach, namely ExDoS, that harvests benefits of both supervised and unsupervised algorithms simultaneously. To the best of our knowledge, ExDoS is the first approach to combine both supervised and unsupervised algorithms in a single framework and an interpretable manner for document summarization purpose. ExDoS iteratively minimizes the error rate of the classifier in each cluster with the help of dynamic local feature weighting. Moreover, this approach specifies the contribution of features to discriminate each class, which is a challenging issue in the summarization task. Therefore, in addition to summarizing text, ExDoS is also able to measure the importance of each feature in the summarization process. We evaluate our model both automatically (in terms of ROUGE factor) and empirically (human analysis) on the benchmark datasets: the DUC2002 and CNN/DailyMail. Results show that our model obtains higher ROUGE scores comparing to most state-of-the-art models. The human evaluation also demonstrates that our model is capable of generating informative and readable summaries.

A Simhash-Based Integrative Features Extraction Algorithm for Malware Detection

In the malware detection process, obfuscated malicious codes cannot be efficiently and accurately detected solely in the dynamic or static feature space. Aiming at this problem, an integrative feature extraction algorithm based on simhash was proposed, which combines the static information e.g., API (Application Programming Interface) calls and dynamic information (such as file, registry and network behaviors) of malicious samples to form integrative features. The experiment extracts the integrative features of some static information and dynamic information, and then compares the classification, time and obfuscated-detection performance of the static, dynamic and integrated features, respectively, by using several common machine learning algorithms. The results show that the integrative features have better time performance than the static features, and better classification performance than the dynamic features, and almost the same obfuscated-detection performance as the dynamic features. This algorithm can provide some support for feature extraction of malware detection.

Sparse reduced-order modelling: sensor-based dynamics to full-state estimation

We propose a general dynamic reduced-order modelling framework for typical experimental data: time-resolved sensor data and optional non-time-resolved particle image velocimetry (PIV) snapshots. This framework can be decomposed into four building blocks. First, the sensor signals are lifted to a dynamic feature space without false neighbours. Second, we identify a sparse human-interpretable nonlinear dynamical system for the feature state based on the sparse identification of nonlinear dynamics (SINDy). Third, if PIV snapshots are available, a local linear mapping from the feature state to the velocity field is performed to reconstruct the full state of the system. Fourth, a generalized feature-based modal decomposition identifies coherent structures that are most dynamically correlated with the linear and nonlinear interaction terms in the sparse model, adding interpretability. Steps 1 and 2 define a black-box model. Optional steps 3 and 4 lift the black-box dynamics to a grey-box model in terms of the identified coherent structures, if non-time-resolved full-state data are available. This grey-box modelling strategy is successfully applied to the transient and post-transient laminar cylinder wake, and compares favourably with a proper orthogonal decomposition model. We foresee numerous applications of this highly flexible modelling strategy, including estimation, prediction and control. Moreover, the feature space may be based on intrinsic coordinates, which are unaffected by a key challenge of modal expansion: the slow change of low-dimensional coherent structures with changing geometry and varying parameters.

Deep ranking model by large adaptive margin learning for person re-identification

Person re-identification aims to match images of the same person across disjoint camera views, which is a challenging problem in video surveillance. The major challenge of this task lies in how to preserve the similarity of the same person against large variations caused by complex backgrounds, mutual occlusions and different illuminations, while discriminating the different individuals. In this paper, we present a novel deep ranking model with feature learning and fusion by learning a large adaptive margin between the intra-class distance and inter-class distance to solve the person re-identification problem. Specifically, we organize the training images into a batch of pairwise samples. Treating these pairwise samples as inputs, we build a novel part-based deep convolutional neural network (CNN) to learn the layered feature representations by preserving a large adaptive margin. As a result, the final learned model can effectively find out the matched target to the anchor image among a number of candidates in the gallery image set by learning discriminative and stable feature representations. Overcoming the weaknesses of conventional fixed-margin loss functions, our adaptive margin loss function is more appropriate for the dynamic feature space. On four benchmark datasets, PRID2011, Market1501, CUHK01 and 3DPeS, we extensively conduct comparative evaluations to demonstrate the advantages of the proposed method over the state-of-the-art approaches in person re-identification.

Mining recurring concepts in a dynamic feature space.

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

A comparative study on feature selection and adaptive strategies for email foldering using the ABC-DynF framework

Email foldering is a challenging problem mainly due to its high dimensionality and dynamic nature. This work presents ABC-DynF, an adaptive learning framework with dynamic feature space that we use to compare several incremental and adaptive strategies to cope with these two difficulties. Several studies have been carried out using datasets from the ENRON email corpus and different configuration settings of the framework. The main aim is to study how feature ranking methods, concept drift monitoring, adaptive strategies and the implementation of a dynamic feature space can affect the performance of Bayesian email classification systems.

Building Recognition Based on Geometric Model in FLIR Image Sequences

In this paper, we propose a novel method for building recognition from forward looking infrared (FLIR) image sequences with clutter in the background for automatic target recognition (ATR). In the first phase, dynamic feature space is defined, and camera model and multi-scale characteristic views model that are used to predict model’s features based on 3D object reference model are introduced. In the second phase, the original image is preprocessed using morphological grayscale filters that respond to the size, shape and orientation of object to suppress the background that contains the non-stationary nature and man-made objects of the clutter image. In the following phase, segmentation for the result of image preprocessing is used to obtain regions of interest (ROIs), and features extraction of ROIs and matching retain ROIs that are closest to predicted features. Lastly, the object is identified by fusing the line features. Experiment results show the algorithm can recognize the object from FLIR image sequences with a complicated background.