The Analysis on Dimensionality Reduction Mathematical Model Based on Feedback Constraint for High-Dimensional Information

2013 ◽  
Vol 846-847 ◽  
pp. 1056-1059
Author(s):  
Peng Wu
Keyword(s):  
Mathematical Model ◽  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Inverse Regression ◽  
Model Based ◽  
Cloud Resource ◽  
Constrained Environments ◽  
Data Coverage ◽  
Cloud Resource Scheduling

This paper proposes a dimensionality reduction mathematical model based on feedback constraint for High-dimensional information. It uses feedback restriction technique to construct dimensionality reduction model for multidimensional product data. The data obtained is with high latitudes, where a large number of data are under components involved standardized restrictions. High-dimensional data participating in operation will increase the complexity of operation, and hence, we need to reduce its dimension. In this paper multi-constrained inverse regression model is adopted to reduce the dimension of cloud resource scheduling data in multi-constrained environments. Experimental results show that the proposed method increases the data coverage rate of high-dimensional data mining by 66%, and has great optimizing effect.

scholarly journals Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sai Kiranmayee Samudrala ◽  
Jaroslaw Zola ◽  
Srinivas Aluru ◽  
Baskar Ganapathysubramanian
Keyword(s):  
Dimensionality Reduction ◽  
Organic Solar Cells ◽  
Large Scale ◽  
Parallel Implementation ◽  
High Dimensional Data ◽  
Real Life ◽  
Processing Parameters ◽  
High Dimensional ◽  
Morphology Evolution ◽  
Reduction Techniques

Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

scholarly journals Identifying Diseases and Diagnosis using Machine Learning

2019 ◽  
Vol 8 (6S2) ◽  
pp. 978-981
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Dimensionality Reduction ◽  
Disease Classification ◽  
High Dimensional ◽  
Classification Algorithms ◽  
Major Work ◽  
The Mathematical Model ◽  
Best Fit ◽  
Learning By Using

The method that is use to optimize the criterion efficiency that depend on the previous experience is known as machine learning. By using the statistics theory it creates the mathematical model, and its major work is to surmise from the examples gave. To take the data straightforwardly from the information the approach uses computational methods. For recognize and identify the disease correctly a pattern is very necessary in Diagnosis recognition of disease. for creating the different models machine learning is used, this model can use for prediction of output and this output is depend on the input that is related to the data which previously used. For curing any disease it is very important to identify and detect that disease. For classify the disease classification algorithms are used. It uses are many dimensionality reduction algorithms and classification algorithms. Without externally modified the computer can learn with the help of the machine learning. For taking the best fit from the observation set the hypothesis is selected. Multi-dimensional and high dimensional are used in machine learning. By using machine learning automatic and classy algorithms can build.

scholarly journals Unsupervised Text Feature Learning via Deep Variational Auto-encoder

2020 ◽  
Vol 49 (3) ◽  
pp. 421-437
Author(s):  
Genggeng Liu ◽  
Lin Xie ◽  
Chi-Hua Chen
Keyword(s):  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Image Data ◽  
Original Data ◽  
Feature Representation ◽  
High Dimensional ◽  
Learning To Learn ◽  
Text Feature ◽  
Reduction Methods ◽  
Low Dimensional

Dimensionality reduction plays an important role in the data processing of machine learning and data mining, which makes the processing of high-dimensional data more efficient. Dimensionality reduction can extract the low-dimensional feature representation of high-dimensional data, and an effective dimensionality reduction method can not only extract most of the useful information of the original data, but also realize the function of removing useless noise. The dimensionality reduction methods can be applied to all types of data, especially image data. Although the supervised learning method has achieved good results in the application of dimensionality reduction, its performance depends on the number of labeled training samples. With the growing of information from internet, marking the data requires more resources and is more difficult. Therefore, using unsupervised learning to learn the feature of data has extremely important research value. In this paper, an unsupervised multilayered variational auto-encoder model is studied in the text data, so that the high-dimensional feature to the low-dimensional feature becomes efficient and the low-dimensional feature can retain mainly information as much as possible. Low-dimensional feature obtained by different dimensionality reduction methods are used to compare with the dimensionality reduction results of variational auto-encoder (VAE), and the method can be significantly improved over other comparison methods.

Glyphboard: Visual Exploration of High-Dimensional Data Combining Glyphs with Dimensionality Reduction

2020 ◽  
Vol 26 (4) ◽  
pp. 1661-1671 ◽  
Author(s):  
Dietrich Kammer ◽  
Mandy Keck ◽  
Thomas Grunder ◽  
Alexander Maasch ◽  
Thomas Thom ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Visual Exploration ◽  
High Dimensional

scholarly journals Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA

2013 ◽  
Vol 7 (3) ◽  
pp. 281-300 ◽  
Author(s):  
Anastasios Bellas ◽  
Charles Bouveyron ◽  
Marie Cottrell ◽  
Jérôme Lacaille
Keyword(s):  
Data Streams ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Probabilistic Pca ◽  
Model Based

scholarly journals Dimensionality Reduction by Weighted Connections between Neighborhoods

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Fuding Xie ◽  
Yutao Fan ◽  
Ming Zhou
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Space ◽  
High Dimensional Data ◽  
Reduction Technique ◽  
Experimental Results ◽  
High Dimensional ◽  
Reduced Dimensionality ◽  
Dimensionality Reduction Technique ◽  
Low Dimensionality ◽  
Local Topology

Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. This paper introduces a dimensionality reduction technique by weighted connections between neighborhoods to improveK-Isomap method, attempting to preserve perfectly the relationships between neighborhoods in the process of dimensionality reduction. The validity of the proposal is tested by three typical examples which are widely employed in the algorithms based on manifold. The experimental results show that the local topology nature of dataset is preserved well while transforming dataset in high-dimensional space into a new dataset in low-dimensionality by the proposed method.

scholarly journals H-tSNE: Hierarchical Nonlinear Dimensionality Reduction

2020 ◽  
Author(s):  
Kevin C. VanHorn ◽  
Murat Can Çobanoğlu
Keyword(s):  
Dimensionality Reduction ◽  
Social Networking ◽  
A Priori ◽  
High Dimensional Data ◽  
High Order ◽  
High Dimensional ◽  
Nonlinear Dimensionality Reduction ◽  
Embedding Technique ◽  
Class Labels ◽  
Parent Child Relationships

AbstractDimensionality reduction (DR) is often integral when analyzing high-dimensional data across scientific, economic, and social networking applications. For data with a high order of complexity, nonlinear approaches are often needed to identify and represent the most important components. We propose a novel DR approach that can incorporate a known underlying hierarchy. Specifically, we extend the widely used t-Distributed Stochastic Neighbor Embedding technique (t-SNE) to include hierarchical information and demonstrate its use with known or unknown class labels. We term this approach “H-tSNE.” Such a strategy can aid in discovering and understanding underlying patterns of a dataset that is heavily influenced by parent-child relationships. Without integrating information that is known a priori, we suggest that DR cannot function as effectively. In this regard, we argue for a DR approach that enables the user to incorporate known, relevant relationships even if their representation is weakly expressed in the dataset.Availabilitygithub.com/Cobanoglu-Lab/h-tSNE

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