A Parallel Implementation of Singular Value Decomposition for Video-on-demand Services Design Using Principal Component Analysis

Ramírez-Velarde, Raúl V.; Roderus, Martin; Barba-Jimenez, Carlos; Perez-Cazares, Raul

doi:10.1016/j.procs.2014.05.172

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…From the comparison, a total of 6 classes (1, 3, 8, 9, 10, 12) defined manually has been identified in 6 different clusters (13,3,2,11,7,14) of the proposed method. Even 4 from 6 clusters (13, 2, 7, 14) have exactly the same or similar all document members with members of the class defined manually (1,8,10,14), because each that clusters took the best value of recall and precision is 1. Furthermore, a total of 6 classes (2,4,5,6,7,11) have the set document members spread to more than one cluster.…”

Section: Results and Analysismentioning

confidence: 99%

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Web News Documents Clustering in Indonesian Language Using Singular Value Decomposition-Principal Component Analysis (Svdpca) and Ant Algorithms

Fadllullah

Kamudi

Nasir

et al. 2016

Jurnal Ilmu Komputer dan Informasi

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Ant-based document clustering is a cluster method of measuring text documents similarity based on the shortest path between nodes (trial phase) and determines the optimal clusters of sequence document similarity (dividing phase). The processing time of trial phase Ant algorithms to make document vectors is very long because of high dimensional Document-Term Matrix (DTM). In this paper, we proposed a document clustering method for optimizing dimension reduction using Singular Value Decomposition-Principal Component Analysis (SVDPCA) and Ant algorithms. SVDPCA reduces size of the DTM dimensions by converting freq-term of conventional DTM to score-pc of Document-PC Matrix (DPCM). Ant algorithms creates documents clustering using the vector space model based on the dimension reduction result of DPCM. The experimental results on 506 news documents in Indonesian language demonstrated that the proposed method worked well to optimize dimension reduction up to 99.7%. We could speed up execution time efficiently of the trial phase and maintain the best Fmeasure achieved from experiments was 0.88 (88%). Keywords: web news documents clustering, principal component analysis, singular value decomposition, dimension reduction, ant algorithms AbstrakKlasterisasi dokumen berbasis algoritma semut merupakan metode klaster yang mengukur kemiripan dokumen teks berdasarkan pencarian rute terpendek antar node (trial phase) dan menentukan sejumlah klaster yang optimal dari urutan kemiripan dokumen (dividing phase). Waktu proses trial phase algoritma semut dalam mengolah vektor dokumen tergolong lama sebagai akibat tingginya dimensi, karena adanya masalah sparseness pada matriks Document-Term Matrix (DTM). Oleh karena itu, penelitian ini mengusulkan sebuah metode klasterisasi dokumen yang mengoptimalkan reduksi dimensi menggunakan Singular Value Decomposition-Principal Component Analysis (SVDPCA) dan Algoritma Semut. SVDPCA mereduksi ukuran dimensi DTM dengan mengkonversi bentuk freq-term DTM konvensional ke dalam bentuk score-pc Document-PC Matrix (DPCM). Kemudian, Algoritma Semut melakukan klasterisasi dokumen menggunakan vector space model yang dibangun berdasarkan DPCM hasil reduksi dimensi. Hasil uji coba dari 506 dokumen berita berbahasa Indonesia membuk-tikan bahwa metode yang diusulkan bekerja dengan baik untuk mengoptimalkan reduksi dimensi hingga 99,7%, sehingga secara efisien mampu mempercepat waktu eksekusi trial phase algoritma se-mut namun tetap mempertahankan akurasi F-measure mencapai 0,88 (88%).Kata Kunci: klasterisasi dokumen web berita, principal component analysis, singular value decomposition, reduksi dimensi, algoritma semut

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Section: Results and Analysismentioning

confidence: 99%

“…In the SVD transformation, the original matrix can be decomposed (similar to PCA eigen-decomposition) into three matrix components with the same size. If they was multiplied again, so they will produce the same value as the original matrix [10]. The decomposition of A is shown in equation(3):…”

Section: Singular Value Decomposition-principal Component Analysis (Smentioning

confidence: 99%

Web News Documents Clustering in Indonesian Language Using Singular Value Decomposition-Principal Component Analysis (Svdpca) and Ant Algorithms

Fadllullah

Kamudi

Nasir

et al. 2016

Jurnal Ilmu Komputer dan Informasi

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Change detection in noisy dynamic networks: a spectral embedding approach

Lee

Moltchanova

McLeod

2020

Soc. Netw. Anal. Min.

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Change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and health care monitoring. It is a challenging problem because it involves a time sequence of graphs, each of which is usually very large and sparse with heterogeneous vertex degrees, resulting in a complex, high dimensional mathematical object. Spectral embedding methods provide an effective way to transform a graph to a lower dimensional latent Euclidean space that preserves the underlying structure of the network. Although change detection methods that use spectral embedding are available, they do not address sparsity and degree heterogeneity that usually occur in noisy real-world graphs and a majority of these methods focus on changes in the behaviour of the overall network.In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes in entity behaviour. Our spectral embedding approach not only addresses sparsity and degree heterogeneity issues, but also obtains an estimate of the appropriate embedding dimension. We call this method CDP (change detection using Procrustes analysis). We demonstrate the performance of CDP through extensive simulation experiments and a real-world application. CDP successfully detects various types of vertex-based changes including (i) changes in vertex degree, (ii) changes in community membership of vertices, and (iii) unusual increase or decrease in edge weight between vertices. The change detection performance 1.2 Hewapathirana et al.of CDP is compared with two other baseline methods that employ alternative spectral embedding approaches. In both cases, CDP generally shows superior performance.A network is a collection of entities, that have inherent relationships. Some examples include a social network of friendships among people, a communication network of company employees connected by phone calls, emails or text messages, and a biological network of neurons connected by their synapses. A network can be mathematically conceptualized as a graph by associating entities with vertices, and relationships with edges connecting vertices in the graph. For example, in the graph representation of a social network like Facebook, vertices may represent friends and edges represent friendship connections.Most real-world networks evolve as time progresses. That is, the entities and their relationships keep evolving with time. This type of relational data can be represented as a dynamic network. For example, a communication network of a company is a dynamic network because new employees (entities) join the network and communication patterns (relationships) are modified continuously. Although both the entities and the relationships in a network can vary over time, in this paper, we assume that a dynamic network consists of a fixed set of entities with time varying relationships between them. A dynamic network can be represente...

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Advanced, hardware supported in-network processing for the internet of things

Alam¹,

Hadjiefthymiades²

2017

Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing

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A Parallel Implementation of Singular Value Decomposition for Video-on-demand Services Design Using Principal Component Analysis

Cited by 5 publications

References 4 publications

Web News Documents Clustering in Indonesian Language Using Singular Value Decomposition-Principal Component Analysis (Svdpca) and Ant Algorithms

Web News Documents Clustering in Indonesian Language Using Singular Value Decomposition-Principal Component Analysis (Svdpca) and Ant Algorithms

Change detection in noisy dynamic networks: a spectral embedding approach

Advanced, hardware supported in-network processing for the internet of things

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