DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 신기정 | - |
dc.contributor.author | Kwon, Taehyung | - |
dc.contributor.author | 권태형 | - |
dc.date.accessioned | 2024-07-22T19:30:08Z | - |
dc.date.available | 2024-07-22T19:30:08Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1044771&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320303 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 김재철AI대학원, 2022.2,[iv, 25 p. :] | - |
dc.description.abstract | Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be reordered without information loss. Storing a sparse matrix in conventional ways requires an amount of space linear in the number of non-zeros, and lossy compression of sparse matrices (e.g., Truncated SVD) typically requires an amount of space sublinear in the number of non-zeros but still linear in the number of rows and columns. In this work, we propose \method for compressing a sparse reorderable matrix, regardless of its size, into a fixed amount of space. NeuKron updates the parameters so that a given matrix is approximated by the product, and NeuKron also reorders the rows and columns of the matrix to facilitate the approximation. Given an $n$-by-$m$ matrix with $p$ non-zeros, where $n \leq m$ without loss of generality, the above update steps, which take $O(m+p\cdot \log m)$ time, are repeated alternatively, and from the trained model, the approximate value of each entry is retrieved in $O(\log m)$ time. Through experiments on six real-world datasets, we demonstrate that NeuKron is (a) Compact: requiring five orders of magnitude less space than its best competitor with similar approximation errors, (b) Accurate: giving up to $10.1\times$ smaller approximation errors than its best competitor with similar space requirements, and (c) Scalable: compressing a matrix with about $230$ million non-zeros. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 그래프 마이닝▼a머신 러닝▼a행렬 압축▼a일반화 된 크로네커 곱 | - |
dc.subject | Graph mining▼amachine learning▼amatrix compression▼ageneralized Kronecker product | - |
dc.title | NeuKron: constant-space lossy compression of sparse reorderable matrices | - |
dc.title.alternative | NeuKron: 상수 개의 파라미터를 사용하는 재배열 가능한 희소 행렬의 손실 압축 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :김재철AI대학원, | - |
dc.contributor.alternativeauthor | Shin, Kijung | - |
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