Quantum k-means algorithm based on Manhattan distance

Wu, Zhihao; Song, Ting-Ting; Zhang, Yanbing

doi:10.1007/s11128-021-03384-7

Cited by 25 publications

(15 citation statements)

References 35 publications

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“…3 Vector representation of each tweet generated by the BERT model K-means computes the centroids and iterates until finding an optimal centroid [27]. The data points are assigned to a cluster so that the sum of the squared distance between the data points and centroid would be minimum [28]. As listed in Table 1, the t-SNE passed k-means has a higher silhouette score than the principal component analysis.…”

Section: F Web Interfacementioning

confidence: 99%

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Ananthapadmanabha¹,

C²,

Sabariraju³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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One in every four individuals has a diagnosable mental disease in a year. Around 20% of children and adolescents have a mental health condition and often ignore it. It is found that 93% of youth use social media to communicate and engage, as it reflects their emotions, moods, and thoughts. As a result, machine learning algorithms may anticipate people's moods and emotions based on their postings and comments. On the other hand, psychometric tests use questions to determine how individuals think, feel, behave, and react. It is necessary to investigate a hybrid approach for identifying people's mental illness by combining social media inputs and psychometric tests, especially during a pandemic. The hybrid approach can combine the results from both the models to reflect on the user's digital & non-digital reactions to certain sensitive situations to determine their mental state. Hence, the present paper aims to develop a web framework that can forecast the emergence of mental illness in the future based on data from social media comments and real-time data from psychometric tests using machine learning algorithms. The proposed work includes the cluster ensemble method for social media posts and a convolution neural network model for psychometric tests. This model predicts mental illness with an accuracy of 87.05 percent. The individual can use this result to take the required precautions by visiting a psychologist.

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Section: F Web Interfacementioning

confidence: 99%

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Ananthapadmanabha¹,

C²,

Sabariraju³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Additionally, the realignment method, originally derived for the quantification of entanglement, was studied in [131] for steering, this is related to the positivity of states containing steering in the sense of the PPT criterion [236,237].…”

Section: Positivity Criteriamentioning

confidence: 99%

Certification and applications of quantum nonlocal correlations

Piceno-Martínez,

Rosales-Zárate,

Ornelas-Cruces

2023

J. Phys. Photonics

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Entanglement and Einstein-Podolsky-Rosen (EPR) steering are nonlocal quantum correlations, which are relevant resources for quantum information protocols. EPR steering, or quantum steering, refers to the correlation where a party might “steer”, or modify, the state of another, which is spatially separated. Entanglement is a symmetric resource while steering is asymmetrical, since it depends on the direction of the effect. Due to these different characteristics and the therefore different possible applications, there has been both theoretical and experimental research on forms to certify the distinct quantum nonlocal correlations. In recent years, alongside the investigation on quantum correlations between two systems, there has been a great interest in investigating multipartite/multimode entanglement as well as steering, since they include a high dimension and it may be possible to store more information than in a single qubit. In this review, we will summarize the different criteria and measures that have been developed for the characterization of these two kinds of correlations. We first focus on bipartite entanglement and steering. We then review the progress that has been made in the investigation of multipartite quantum correlations. We revise the theoretical work in quantum nonlocal correlation witnesses and measures, which respectively allow one to certify that the system is entangled or presents EPR steering, and give a quantification of the content of these correlations in the system. Then, we briefly review the experiments that have been designed and that demonstrate multipartite quantum correlations. We also include applications in quantum information protocols, in particular in quantum teleportation and quantum cryptography.

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“…It can only mine the linear relationship in the data, but not the nonlinear relationship in the data. 3. In practical applications, data labels are often missing, and it is not suitable for such situations.…”

Section: Proposed Piecewise Weighted With Hyper-class Representationmentioning

confidence: 99%

“…However, the existing similarity calculations, such as Euclidean distance [2], Manhattan distance [3] and cosine distance [4], all operate on the value of the corresponding attribute, which ignores the importance of each attribute [5]. Because there are redundant attributes in the data and different attributes have different weights.…”

Section: Introductionmentioning

confidence: 99%

Piecewise Weighting Function for Collaborative Filtering Recommendation

Zhang¹,

Li²

2022

Preprint

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<p>It is not always reasonable to set a fixed weight value to an attribute (or a variable) because this cannot accurately maintain the similarity between users and leads to decrease the performance of collaborative filtering recommendation algorithms. In this paper, a piecewise weighting method is proposed with the hyper-class representation for improving the collaborative filtering recommendation. Specifically, a kernel function is first applied to map the original data into the kernel space, and to learn the weight of each attribute. And then, the hyper-class representation of the data is constructed to learn the weight of the segmented attribute value (hyper-class) for each attribute, so as to construct a piecewise weighting function. In addition, the piecewise weighting function is applied to calculate the similarity between users for a collaborative filtering recommendation. Finally, experiments are conducted to examine the collaborative filtering recommendation algorithm, and show that the proposed algorithm with piecewise weighting functions is more efficient than the compared algorithm with fixed weight values, in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Precision.</p>

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Quantum k-means algorithm based on Manhattan distance

Cited by 25 publications

References 35 publications

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Certification and applications of quantum nonlocal correlations

Piecewise Weighting Function for Collaborative Filtering Recommendation

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