miningDatapre-processingMulti-dimensionalsamplingCopulasDimensionalityreductionThe recent trends in collecting huge and diverse datasets have created a great challenge in data analysis. One of the characteristics of these gigantic datasets is that they often have significant amounts of redundancies. The use...
Survey on distance metric learning and dimensionality reduction in data mining. Data Min Knowl Disc 29, 534–564 (2015). https://doi.org/10.1007/s10618-014-0356-z Download citation Received06 November 2012 Accepted24 May 2014 Published27 June 2014 Issue DateMarch 2015 DOIhttps://doi.org/...
It is so easy and convenient to collect data An experiment Data is not collected only for data mining Data accumulates in an unprecedented speed Data preprocessing is an important part for effective machine learning and data mining Dimensionality reduction is an effective approach to downsizing data ...
This is how the dimensionality reduction technique is used to compress complex data into a simpler form without losing the essence of the data. Moreover, data science and AI experts are now also usingdata science solutionsto leverage business ROI. Data visualization, data mining, predictive analyti...
A multivariate time series is one of the most important objects of research in data mining. Time and variables are two of its distinctive characteristics that add the complication of the algorithms applied to data mining. Reduction i...
Data reductionDimensionality reductionPurpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data ...
3.0.3Dimensionality reduction Dimensionality reductionis a necessary process in mostbig datarecognition frameworks that tackles the problem of learning and trainability of the model in the design. Essentially, dimensionality reduction is often seen as a drawback to mostsystem architecturesbecause it elimina...
Many statistical learning tasks deal with data which are presented in high-dimensional spaces, and the 'curse of dimensionality' phenomenon is often an obstacle to the use of many methods for solving these tasks. To avoid this phenomenon, various dimensionality reduction algorithms are used as the...
Dimensionality reduction for density ratio estimation in high-dimensional spaces. The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it c... M Sugiyama,M Kawanabe,PL Chui - 《Neural Networks》 被引...
An improved incremental nonlinear dimensionality reduction for isometric data embedding Summary: Manifold learning has become a hot issue in the field of machine learning and data mining. There are some algorithms proposed to extract the intri... X Gao,J Liang - 《Information Processing Letters》 ...