Decoding intelligence via symmetry and asymmetry

Fu, Jianjing; Hsiao, Ching-an

doi:10.1038/s41598-024-62906-2

Sci Rep

2024

DOI: 10.1038/s41598-024-62906-2

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Decoding intelligence via symmetry and asymmetry

Jianjing Fu,

Ching-an Hsiao

Abstract: Humans use pictures to model the world. The structure of a picture maps to mind space to form a concept. When an internal structure matches the corresponding external structure, an observation functions. Whether effective or not, the observation is self-consistent. In epistemology, people often differ from each other in terms of whether a concept is probabilistic or certain. Based on the effect of the presented IG and pull anti algorithm, we attempt to provide a comprehensive answer to this problem. Using the … Show more

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2024

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Cited by 2 publications

References 42 publications

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Sparse Fuzzy C-Means Clustering with Lasso Penalty

Parveen,

Yang

2024

Symmetry

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Clustering is a technique of grouping data into a homogeneous structure according to the similarity or dissimilarity measures between objects. In clustering, the fuzzy c-means (FCM) algorithm is the best-known and most commonly used method and is a fuzzy extension of k-means in which FCM has been widely used in various fields. Although FCM is a good clustering algorithm, it only treats data points with feature components under equal importance and has drawbacks for handling high-dimensional data. The rapid development of social media and data acquisition techniques has led to advanced methods of collecting and processing larger, complex, and high-dimensional data. However, with high-dimensional data, the number of dimensions is typically immaterial or irrelevant. For features to be sparse, the Lasso penalty is capable of being applied to feature weights. A solution for FCM with sparsity is sparse FCM (S-FCM) clustering. In this paper, we propose a new S-FCM, called S-FCM-Lasso, which is a new type of S-FCM based on the Lasso penalty. The irrelevant features can be diminished towards exactly zero and assigned zero weights for unnecessary characteristics by the proposed S-FCM-Lasso. Based on various clustering performance measures, we compare S-FCM-Lasso with the S-FCM and other existing sparse clustering algorithms on several numerical and real-life datasets. Comparisons and experimental results demonstrate that, in terms of these performance measures, the proposed S-FCM-Lasso performs better than S-FCM and existing sparse clustering algorithms. This validates the efficiency and usefulness of the proposed S-FCM-Lasso algorithm for high-dimensional datasets with sparsity.

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Sparse Fuzzy C-Means Clustering with Lasso Penalty

Parveen,

Yang

2024

Symmetry

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Weighted Multiview K-Means Clustering with L2 Regularization

Hussain,

Nataliani,

Ali

et al. 2024

Symmetry

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In the era of big data, cloud, internet of things, virtual communities, and interconnected networks, the prominence of multiview data is undeniable. This type of data encapsulates diverse feature components across varying perspectives, each offering unique insights into the same underlying samples. Despite being sourced from diverse settings and domains, these data serve the common purpose of describing the same samples, establishing a significant interrelation among them. Thus, there arises a necessity for the development of multiview clustering methodologies capable of leveraging the wealth of information available across multiple views. This study introduces two novel weighted multiview k-means algorithms, W-MV-KM and weighted multiview k-means using L2 regularization, W-MV-KM-L2, designed specifically for clustering multiview data. These algorithms incorporate feature weights and view weights within the k-means (KM) framework. Our approach emphasizes a weighted multiview learning strategy, which assigns varying degrees of importance to individual views. We evaluate the clustering performance of our algorithms on seven diverse benchmark datasets spanning dermatology, textual, image, and digit domains. Through extensive experimentation and comparisons with existing methods, we showcase the superior effectiveness and utility of our newly introduced W-MV-KM-L2 algorithm.

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Decoding intelligence via symmetry and asymmetry

Cited by 2 publications

References 42 publications

Sparse Fuzzy C-Means Clustering with Lasso Penalty

Sparse Fuzzy C-Means Clustering with Lasso Penalty

Weighted Multiview K-Means Clustering with L2 Regularization

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