Neural Membrane Mutual Coupling Characterisation Using Entropy-Based Iterative Learning Identification

Multi-view clustering (MVC) is a mainstream task that aims to divide objects into meaningful groups from different perspectives. The quality of data representation is the key issue in MVC. A comprehensive meaningful data representation should be with the discriminant characteristics in a single view and the correlation of multiple views. Considering this, a novel framework called Dynamic Guided Metric Representation Learning for Multi-View Clustering (DGMRL-MVC) is proposed in this paper, which can cluster multi-view data in a learned latent discriminated embedding space. Specifically, in the framework, the data representation can be enhanced by multi-steps. Firstly, the class separability is enforced with Fisher Discriminant Analysis (FDA) within each single view, while the consistence among different views is enhanced based on Hilbert-Schmidt independence criteria (HSIC). Then, the 1st enhanced representation is obtained. In the second step, a dynamic routing mechanism is introduced, in which the location or direction information is added to fulfil the expression. After that, a generalized canonical correlation analysis (GCCA) model is used to get the final ultimate common discriminated representation. The learned fusion representation can substantially improve multi-view clustering performance. Experiments validated the effectiveness of the proposed method for clustering tasks.

“…, Wang, Zheng & Li, 2017 ), unified multi view fusion model design and optimization ( e.g. , Tang et al, 2020 ), and et al In the future, we will do more work on multi view data fusion.…”

Section: Discussionmentioning

confidence: 99%

Dynamic guided metric representation learning for multi-view clustering

Zheng

Zhang

Wang

2022

“…Traditionally, researchers treat stock price prediction as a time-series problem and solve it with classic RNN models such as LSTM. Existing researches show that LSTM can effectively extract time-series information and has good performance in stock price prediction ( Cao, Li & Li, 2019 ; Chen & Ge, 2019 ; Fischer & Krauss, 2018 ; Kim & Won, 2018 ; Zhang, Aggarwal & Qi, 2017 ; Bao, Yue & Rao, 2017 ; Tang et al, 2020 ; Rezaei, Faaljou & Mansourfar, 2021 ; Zhao et al, 2021 ). For example, Fischer & Krauss (2018) made an earlier attempt to use LSTMs for stock selection of S&P 500 constituents based on the time series characteristics.…”

Section: Related Workmentioning

confidence: 99%

GCN-based stock relations analysis for stock market prediction

Zhao

Liu

Zhou

et al. 2022

Most stock price predictive models merely rely on the target stock’s historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility.

“…(11) is the graph affinity matrix; therefore, they share the identical optimization procedure. Below taking the KMSR-G as an example, we show its detailed optimization steps based on the alternating framework ( Tang et al, 2020 ). That is, we update one variable by fixing the others.…”

Section: The Proposed Modelmentioning

confidence: 99%

A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation

Huang

Peng

et al. 2021

The Kmeans clustering and spectral clustering are two popular clustering methods for grouping similar data points together according to their similarities. However, the performance of Kmeans clustering might be quite unstable due to the random initialization of the cluster centroids. Generally, spectral clustering methods employ a two-step strategy of spectral embedding and discretization postprocessing to obtain the cluster assignment, which easily lead to far deviation from true discrete solution during the postprocessing process. In this paper, based on the connection between the Kmeans clustering and spectral clustering, we propose a new Kmeans formulation by joint spectral embedding and spectral rotation which is an effective postprocessing approach to perform the discretization, termed KMSR. Further, instead of directly using the dot-product data similarity measure, we make generalization on KMSR by incorporating more advanced data similarity measures and call this generalized model as KMSR-G. An efficient optimization method is derived to solve the KMSR (KMSR-G) model objective whose complexity and convergence are provided. We conduct experiments on extensive benchmark datasets to validate the performance of our proposed models and the experimental results demonstrate that our models perform better than the related methods in most cases.