From Whole to Part: Reference-Based Representation for Clustering Categorical Data

Zheng, Qibin; Diao, Xingchun; Cao, Jianjun; Liu, Yi; Li, Hongmei; Yao, Junnan; Chang, Chen; Lv, Guorong

doi:10.1109/tnnls.2019.2911118

Cited by 11 publications

(9 citation statements)

References 26 publications

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“…In equation ( 8), the dimensionality is very high for large data sets, which leads to high computational complexity of the representation and the follow-up task. Given a data set X ∈ R n×d , the representing complexity is O(n 2 ) [35]. Besides, it is not true that all the samples are useful for the representation, and some of them may undermine the representing ability.…”

Section: Semantic Structure-based Representation For Single-modality Datamentioning

confidence: 99%

“…Besides, it is not true that all the samples are useful for the representation, and some of them may undermine the representing ability. Zheng et al [35] believed that it is better to take some representative samples as the reference points rather than all of them and propose a lower-dimensional SSR-the reference-based representation. As illustrated in Figure 3, six purple points are represented as their similarities (the dotted line) to three reference points (the orange ones).…”

Section: Semantic Structure-based Representation For Single-modality Datamentioning

confidence: 99%

“…where x r ij′ is the cosine similarity between x i and the reference point x j′ ∈ X ′ . e reference set X ′ is a subset of X, which is selected by a clustering-based strategy in [35]. As Figure 4 shows, X is divided into groups through clustering, and the center of each cluster is selected as a reference point.…”

Section: Semantic Structure-based Representation For Single-modality Datamentioning

confidence: 99%

“…Moreover, following the framework above, we proposed a cross-modal retrieval method based on the reference-based representation and the correlation between the semantic structures of different modalities. Specifically, Abstraction is implemented by the reference-based representation [35], which represents multimodal objects through the semantic structure. e term semantic structure refers to all pairwise similarities of a set of n samples, for some similarity measure [36].…”

Section: Introductionmentioning

confidence: 99%

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Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

Zheng

Ren

Liu

et al. 2020

Mathematical Problems in Engineering

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Cross-modal retrieval aims to find relevant data of different modalities, such as images and text. In order to bridge the modality gap, most existing methods require a lot of coupled sample pairs as training data. To reduce the demands for training data, we propose a cross-modal retrieval framework that utilizes both coupled and uncoupled samples. The framework consists of two parts: Abstraction that aims to provide high-level single-modal representations with uncoupled samples; then, Association links different modalities through a few coupled training samples. Moreover, under this framework, we implement a cross-modal retrieval method based on the consistency between the semantic structure of multiple modalities. First, both images and text are represented with the semantic structure-based representation, which represents each sample as its similarity from the reference points that are generated from single-modal clustering. Then, the reference points of different modalities are aligned through an active learning strategy. Finally, the cross-modal similarity can be measured with the consistency between the semantic structures. The experiment results demonstrate that given proper abstraction of single-modal data, the relationship between different modalities can be simplified, and even limited coupled cross-modal training data are sufficient for satisfactory retrieval accuracy.

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Section: Semantic Structure-based Representation For Single-modality Datamentioning

confidence: 99%

Section: Semantic Structure-based Representation For Single-modality Datamentioning

confidence: 99%

Section: Semantic Structure-based Representation For Single-modality Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

Zheng

Ren

Liu

et al. 2020

Mathematical Problems in Engineering

Self Cite

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“…For example, the firefly algorithm was used to generate initial clusters [20]. Some scholars started from the data themselves, and studied the possibility of reducing the dimension of representation based on spatial structure while maintaining the same representation capability [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data

Jiang

Liu

2020

Processes

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In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. This P system can prevent the clustering algorithm falling into the local optimum and realize the clustering process in implicit parallelism. The consensus fuzzy k-modes algorithm can combine the advantages of the fuzzy k-modes algorithm, weight fuzzy k-modes algorithm and genetic fuzzy k-modes algorithm. The fuzzy k-modes algorithm can realize the soft partition which is closer to reality, but treats all the variables equally. The weight fuzzy k-modes algorithm introduced the weight vector which strengthens the basic k-modes clustering by associating higher weights with features useful in analysis. These two methods are only improvements the k-modes algorithm itself. So, the genetic k-modes algorithm is proposed which used the genetic operations in the clustering process. In this paper, we examine these three kinds of k-modes algorithms and further introduce DNA genetic optimization operations in the final consensus process. Finally, we conduct experiments on the seven UCI datasets and compare the clustering results with another four categorical clustering algorithms. The experiment results and statistical test results show that our method can get better clustering results than the compared clustering algorithms, respectively.

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From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization

Orlov,

Akhmetshin,

Horvath

et al. 2024

Molecular Informatics

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Dimensionality reduction is an important exploratory data analysis method that allows high‐dimensional data to be represented in a human‐interpretable lower‐dimensional space. It is extensively applied in the analysis of chemical libraries, where chemical structure data ‐ represented as high‐dimensional feature vectors‐are transformed into 2D or 3D chemical space maps. In this paper, commonly used dimensionality reduction techniques ‐ Principal Component Analysis (PCA), t‐Distributed Stochastic Neighbor Embedding (t‐SNE), Uniform Manifold Approximation and Projection (UMAP), and Generative Topographic Mapping (GTM) ‐ are evaluated in terms of neighborhood preservation and visualization capability of sets of small molecules from the ChEMBL database.

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From Whole to Part: Reference-Based Representation for Clustering Categorical Data

Cited by 11 publications

References 26 publications

Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data

From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization

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