Temat: SPACE method - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Latent semantic indexing using eigenvalue analysis for efficient information retrieval
Autorzy:: Aswani Kumar, Ch.
Srinivas, S.
Powiązania:: https://bibliotekanauki.pl/articles/908375.pdf
Data publikacji:: 2006
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: wyszukiwanie informacji
indeksowanie semantyczne
wartość własna
wektor przestrzenny
information retrieval
latent semantic indexing
eigenvalues
rank reduction
singular value decomdecomposition
vector space method
Opis:: Text retrieval using Latent Semantic Indexing (LSI) with truncated Singular Value Decomposition (SVD) has been intensively studied in recent years. However, the expensive complexity involved in computing truncated SVD constitutes a major drawback of the LSI method. In this paper, we demonstrate how matrix rank approximation can influence the effectiveness of information retrieval systems. Besides, we present an implementation of the LSI method based on an eigenvalue analysis for rank approximation without computing truncated SVD, along with its computational details. Significant improvements in computational time while maintaining retrieval accuracy are observed over the tested document collections.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2006, 16, 4; 551-558
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A feasible k-means kernel trick under non-Euclidean feature space
Autorzy:: Kłopotek, Robert
Kłopotek, Mieczysław
Wierzchoń, Sławomir
Powiązania:: https://bibliotekanauki.pl/articles/1838163.pdf
Data publikacji:: 2020
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: kernel method
k-means
non-Euclidean feature space
Gower and Legendre theorem
Opis:: This paper poses the question of whether or not the usage of the kernel trick is justified. We investigate it for the special case of its usage in the kernel k-means algorithm. Kernel-k-means is a clustering algorithm, allowing clustering data in a similar way to k-means when an embedding of data points into Euclidean space is not provided and instead a matrix of “distances” (dissimilarities) or similarities is available. The kernel trick allows us to by-pass the need of finding an embedding into Euclidean space. We show that the algorithm returns wrong results if the embedding actually does not exist. This means that the embedding must be found prior to the usage of the algorithm. If it is found, then the kernel trick is pointless. If it is not found, the distance matrix needs to be repaired. But the reparation methods require the construction of an embedding, which first makes the kernel trick pointless, because it is not needed, and second, the kernel-k-means may return different clusterings prior to repairing and after repairing so that the value of the clustering is questioned. In the paper, we identify a distance repairing method that produces the same clustering prior to its application and afterwards and does not need to be performed explicitly, so that the embedding does not need to be constructed explicitly. This renders the kernel trick applicable for kernel-k-means.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2020, 30, 4; 703-715
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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