dimensionality reduction 中文意思是什麼

dimensionality reduction 解釋
降維
  • dimensionality : 尺度線度
  • reduction : n 1 縮小，減少；降級，降位；（刑罰等的）輕減；減速；減價，折扣。2 （城市、國家等的）陷落，投降，...
  1. The work of the paper mainly includes : ( 1 ) present a model for measuring the similarity between two hydrological time series. in this model, we adopt an intuitive dimensionality reduction technique for hydrological time series which is called piecewise average approximation ( paa )

    主要工作包括： （ 1 ）提出了適合水文時間序列數據特點的相似性模型，採用簡單直觀的等時間間隔序列分段平均值技術（ paa ）作為水文時間序列降維方法。
  2. Band selection methods are more proper for hardware realization and the last method is chosen for dimensionality reduction of hyperspectral image processed by the hardware platform. secondly, rx, gmrf and sem, three representational algorithms of anomaly detection are studied

    其次，研究了具有代表性的三種奇異檢測演算法: rx演算法、基於高斯馬爾可夫隨機場模型（ gmrf ）的檢測演算法和基於隨機最大期望（ sem ）分類的檢測演算法。
  3. There are two kinds of dimensionality reduction methods, band selection and feature extraction

    降維方法主要有波段選擇和特徵提取兩大類方法。
  4. Principal component analysis ( pca ), segmented pincipal components transform ( spct ), band selection based on genetic algorithm ( ga ) and high order component are introduced here, which are all effective in for dimensionality reduction

    本文介紹了主分量分析、分段主成分變換、基於遺傳演算法和高階矩的波段選擇四種降維方法。
  5. Fast and high - quality document clustering algorithms play an important role towards this goal as they have been shown to provide both an navigation / browsing mechanism by organizing large amounts of information into a small number of meaningful clusters as well as to greatly improve the retrieval performance either via cluster - driven dimensionality reduction or term - weighting

    快速和高質量的文本聚類技術在實現這個目標過程中扮演了重要的角色。通過將大量信息組織成少數有意義的簇，這種技術能夠提供導航瀏覽機制，或者，通過聚類驅動的降維或權值調整來極大地改善檢索性能。
  6. Adopting the globe pole mapping method of space analytic geometry, forming a topological mapping model from the high dimensionality vector to the low one, and then realizing a corresponding mapping from the rectangular matrix high dimensionality space text set to the low dimensionality space text set, finally, composing the corresponding arithmetics, accordingly solving the problem of nonlinear dimensionality reduction for text mining effectively, and overcoming some drawbacks in the former researches

    摘要採用了空間解析幾何中的球極映射方法，形成高維向量到低維向量的拓撲變換模型，實現了矩陣形式的高維空間文本集合到低維空間文本集合的一一映射，編制了相應的演算法，從而有效地解決了文本挖掘中的非線性降維問題，克服了以往研究中的缺陷。
  7. Pochet, n., et al. " systematic benchmarking of microarray data classification : assessing the role of nonlinearity and dimensionality reduction. " bioinformatics, 2004

    基因晶元數據分類的系統基準：評估非線性和維度的縮減。 《生物信息學》 ， 2004年。
  8. 15 george karypis, euihong han. fast supervised dimensionality reduction algorithm with applications to document categorization retrieval

    具體做法就是，先採用拉推策略來修正類中心，然後把修正的類中心作為壓縮空間的坐標。
  9. And there is no need to use every band to do classification and targets identify, so it is necessary to do dimensionality reduction first

    目標識別和分類等圖像處理並不一定需要全部的波段來進行，因此對高光譜圖像進行數據降維是十分必要的。
  10. This paper researches dimensionality reduction both from feature extraction and selection techniques. this paper first introduce basic conception and physics elements about hyperspectral image, at the same time introduce the basic conception of feature extraction and selection techniques and the research status in quo, and classification, target detection, then analysis character of hyperspectral image, including its data expression and three characters

    論文首先介紹了高光譜圖像的基本概念和物理原理，同時介紹了特徵提取和特徵選擇技術的基本概念及研究現狀，以及分類和目標檢測等相關技術，然後分析了高光譜圖像的特性，包括它的數據表示方式，信息獲取以及三個顯著特點。
  11. This paper deeply studies the manifold learning method called locally linear embedding ( lle ) and improves it. the main achievements in this paper are as follows : 1. it summarizes the development of manifold learning currently, analyzes the characteristic of nonlinear dimensionality reduction methods, compares the virtues and drawbacks, and makes correlative computer experiments

    本文主要對基於流形學習的局部線性嵌入（ lle ）演算法進行了深入的研究與改進，具體工作包括以下四部分: 1 .簡要綜述了當前流形學習的發展概況，對現有各種非線性降維方法的特點進行分析，比較優點和不足，並進行了相關的計算機模擬實驗。
  12. One is the performance of data mining algorithms degrades, the other is many distance - based and density - based algorithms maybe not effective. these problems can be solved by the following methods : l ) transport the data from high dimensional space to lower dimensional space by dimensionality reduction, then process the data as lower dimensional data. 2 ) to improve the performance of mining algorithms, we can design more effective indexing structures, adopt incremental algorithms and parallel algorithms and so on

    解決的方法可以有以下幾種：一個可以通過降維將數據從高維降到低維，然後用低維數據的處理辦法進行處理；對演算法效率下降問題可以通過設計更為有效的索引結構、採用增量演算法及并行演算法等來提高演算法的性能；對失效的問題通過重新定義使其獲得新生。
分享友人
分享到 Line

分享到臉書