scholarly journals Linear Manifold Regularization with Adaptive Graph for Semi-supervised Dimensionality Reduction

2017 ◽  
Author(s):  
Kai Xiong ◽  
Feiping Nie ◽  
Junwei Han
Keyword(s):  
Dimensionality Reduction ◽  
Objective Function ◽  
Linear Manifold ◽  
Original Data ◽  
Manifold Regularization ◽  
Redundant Information ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Optimal Graph ◽  
Subsequent Task

Many previous graph-based methods perform dimensionality reduction on a pre-defined graph. However, due to the noise and redundant information in the original data, the pre-defined graph has no clear structure and may not be appropriate for the subsequent task. To overcome the drawbacks, in this paper, we propose a novel approach called linear manifold regularization with adaptive graph (LMRAG) for semi-supervised dimensionality reduction. LMRAG directly incorporates the graph construction into the objective function, thus the projection matrix and the optimal graph can be simultaneously optimized. Due to the structure constraint, the learned graph is sparse and has clear structure. Extensive experiments on several benchmark datasets demonstrate the effectiveness of the proposed method.

scholarly journals Locality Adaptive Discriminant Analysis

2017 ◽  
Author(s):  
Xuelong Li ◽  
Mulin Chen ◽  
Feiping Nie ◽  
Qi Wang
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Real World ◽  
Original Data ◽  
Additional Parameter ◽  
Distributed Data ◽  
Local Data ◽  
Linear Discriminant ◽  
Benchmark Datasets ◽  
Neighbor Relationship

Linear Discriminant Analysis (LDA) is a popular technique for supervised dimensionality reduction, and its performance is satisfying when dealing with Gaussian distributed data. However, the neglect of local data structure makes LDA inapplicable to many real-world situations. So some works focus on the discriminant analysis between neighbor points, which can be easily affected by the noise in the original data space. In this paper, we propose a new supervised dimensionality reduction method, Locality Adaptive Discriminant Analysis (LADA), to lean a representative subspace of the data. Compared to LDA and its variants, the proposed method has three salient advantages: (1) it finds the principle projection directions without imposing any assumption on the data distribution; (2) it’s able to exploit the local manifold structure of data in the desired subspace; (3) it exploits the points’ neighbor relationship automatically without introducing any additional parameter to be tuned. Performance on synthetic datasets and real-world benchmark datasets demonstrate the superiority of the proposed method.

scholarly journals Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR

2021 ◽  
Vol 11 (11) ◽  
pp. 5028
Author(s):  
Miaomiao Sun ◽  
Zhenchun Li ◽  
Yanli Liu ◽  
Jiao Wang ◽  
Yufei Su
Keyword(s):  
Reflection Coefficient ◽  
Compressed Sensing ◽  
Objective Function ◽  
Low Frequency ◽  
Original Data ◽  
Seismic Exploration ◽  
High Signal ◽  
Inversion Process ◽  
Frequency Information ◽  
Low Snr

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

Astrid

2020 ◽  
Vol 14 (4) ◽  
pp. 471-484
Author(s):  
Suraj Shetiya ◽  
Saravanan Thirumuruganathan ◽  
Nick Koudas ◽  
Gautam Das
Keyword(s):  
Deep Learning ◽  
Objective Function ◽  
Pattern Matching ◽  
Language Processing ◽  
Language Model ◽  
Language Models ◽  
Selectivity Estimation ◽  
Statistical Correlations ◽  
Benchmark Datasets ◽  
Traditional Approaches

Accurate selectivity estimation for string predicates is a long-standing research challenge in databases. Supporting pattern matching on strings (such as prefix, substring, and suffix) makes this problem much more challenging, thereby necessitating a dedicated study. Traditional approaches often build pruned summary data structures such as tries followed by selectivity estimation using statistical correlations. However, this produces insufficiently accurate cardinality estimates resulting in the selection of sub-optimal plans by the query optimizer. Recently proposed deep learning based approaches leverage techniques from natural language processing such as embeddings to encode the strings and use it to train a model. While this is an improvement over traditional approaches, there is a large scope for improvement. We propose Astrid, a framework for string selectivity estimation that synthesizes ideas from traditional and deep learning based approaches. We make two complementary contributions. First, we propose an embedding algorithm that is query-type (prefix, substring, and suffix) and selectivity aware. Consider three strings 'ab', 'abc' and 'abd' whose prefix frequencies are 1000, 800 and 100 respectively. Our approach would ensure that the embedding for 'ab' is closer to 'abc' than 'abd'. Second, we describe how neural language models could be used for selectivity estimation. While they work well for prefix queries, their performance for substring queries is sub-optimal. We modify the objective function of the neural language model so that it could be used for estimating selectivities of pattern matching queries. We also propose a novel and efficient algorithm for optimizing the new objective function. We conduct extensive experiments over benchmark datasets and show that our proposed approaches achieve state-of-the-art results.

A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jitendra Singh Bhadoriya ◽  
Atma Ram Gupta
Keyword(s):  
Energy Storage ◽  
Objective Function ◽  
Distributed Generation ◽  
Carbon Emissions ◽  
Distribution Network ◽  
Clean Energy ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Novel Approach ◽  
Demand Models

Abstract In recent times, producing electricity with lower carbon emissions has resulted in strong clean energy incorporation into the distribution network. The technical development of weather-driven renewable distributed generation units, the global approach to reducing pollution emissions, and the potential for independent power producers to engage in distribution network planning (DNP) based on the participation in the increasing share of renewable purchasing obligation (RPO) are some of the essential reasons for including renewable-based distributed generation (RBDG) as an expansion investment. The Grid-Scale Energy Storage System (GSESS) is proposed as a promising solution in the literature to boost the energy storage accompanied by RBDG and also to increase power generation. In this respect, the technological, economic, and environmental evaluation of the expansion of RBDG concerning the RPO is formulated in the objective function. Therefore, a novel approach to modeling the composite DNP problem in the regulated power system is proposed in this paper. The goal is to increase the allocation of PVDG, WTDG, and GSESS in DNP to improve the quicker retirement of the fossil fuel-based power plant to increase total profits for the distribution network operator (DNO), and improve the voltage deviation, reduce carbon emissions over a defined planning period. The increment in RPO and decrement in the power purchase agreement will help DNO to fulfill round-the-clock supply for all classes of consumers. A recently developed new metaheuristic transient search optimization (TSO) based on electrical storage elements’ stimulation behavior is implemented to find the optimal solution for multi-objective function. The balance between the exploration and exploitation capability makes the TSO suitable for the proposed power flow problem with PVDG, WTDG, and GSESS. For this research, the IEEE-33 and IEEE-69 low and medium bus distribution networks are considered under a defined load growth for planning duration with the distinct load demand models’ aggregation. The findings of the results after comparing with well-known optimization techniques DE and PSO confirm the feasibility of the method suggested.

scholarly journals Multiple Saliency and Channel Sensitivity Network for Aggregated Convolutional Feature

2019 ◽  
Vol 33 ◽  
pp. 9013-9020
Author(s):  
Xuanlu Xiang ◽  
Zhipeng Wang ◽  
Zhicheng Zhao ◽  
Fei Su
Keyword(s):  
State Of The Art ◽  
Image Representation ◽  
Training Data ◽  
Gram Matrix ◽  
Redundant Information ◽  
Deep Architecture ◽  
Benchmark Datasets ◽  
Supervised Methods ◽  
Ranking Loss ◽  
Effective Channel

In this paper, aiming at two key problems of instance-level image retrieval, i.e., the distinctiveness of image representation and the generalization ability of the model, we propose a novel deep architecture - Multiple Saliency and Channel Sensitivity Network(MSCNet). Specifically, to obtain distinctive global descriptors, an attention-based multiple saliency learning is first presented to highlight important details of the image, and then a simple but effective channel sensitivity module based on Gram matrix is designed to boost the channel discrimination and suppress redundant information. Additionally, in contrast to most existing feature aggregation methods, employing pre-trained deep networks, MSCNet can be trained in two modes: the first one is an unsupervised manner with an instance loss, and another is a supervised manner, which combines classification and ranking loss and only relies on very limited training data. Experimental results on several public benchmark datasets, i.e., Oxford buildings, Paris buildings and Holidays, indicate that the proposed MSCNet outperforms the state-of-the-art unsupervised and supervised methods.

scholarly journals Finding the Number of Clusters in Data and Better Initial Centers for K-means Algorithm

2020 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Ahmed Fahim ◽  
Keyword(s):  
Data Clustering ◽  
Linear Time ◽  
Original Data ◽  
Local Minima ◽  
Expected Number ◽  
Open Problems ◽  
Number Of Clusters ◽  
Benchmark Datasets ◽  
Selection Of

The k-means is the most well-known algorithm for data clustering in data mining. Its simplicity and speed of convergence to local minima are the most important advantages of it, in addition to its linear time complexity. The most important open problems in this algorithm are the selection of initial centers and the determination of the exact number of clusters in advance. This paper proposes a solution for these two problems together; by adding a preprocess step to get the expected number of clusters in data and better initial centers. There are many researches to solve each of these problems separately, but there is no research to solve both problems together. The preprocess step requires o(n log n); where n is size of the dataset. This preprocess step aims to get initial portioning of data without determining the number of clusters in advance, then computes the means of initial clusters. After that we apply k-means on original data using the resulting information from the preprocess step to get the final clusters. We use many benchmark datasets to test the proposed method. The experimental results show the efficiency of the proposed method.

scholarly journals Weakly-Supervised Multi-view Multi-instance Multi-label Learning

2020 ◽  
Author(s):  
Yuying Xing ◽  
Guoxian Yu ◽  
Jun Wang ◽  
Carlotta Domeniconi ◽  
Xiangliang Zhang
Keyword(s):  
Objective Function ◽  
Dictionary Learning ◽  
Function Optimization ◽  
Learning To Learn ◽  
Complex Objects ◽  
Representational Space ◽  
Model Complex ◽  
Feature Similarity ◽  
Benchmark Datasets ◽  
Weakly Supervised

Multi-view, Multi-instance, and Multi-label Learning (M3L) can model complex objects (bags), which are represented with different feature views, made of diverse instances, and annotated with discrete non-exclusive labels. Existing M3L approaches assume a complete correspondence between bags and views, and also assume a complete annotation for training. However, in practice, neither the correspondence between bags, nor the bags' annotations are complete. To tackle such a weakly-supervised M3L task, a solution called WSM3L is introduced. WSM3L adapts multimodal dictionary learning to learn a shared dictionary (representational space) across views and individual encoding vectors of bags for each view. The label similarity and feature similarity of encoded bags are jointly used to match bags across views. In addition, it replenishes the annotations of a bag based on the annotations of its neighborhood bags, and introduces a dispatch and aggregation term to dispatch bag-level annotations to instances and to reversely aggregate instance-level annotations to bags. WSM3L unifies these objectives and processes in a joint objective function to predict the instance-level and bag-level annotations in a coordinated fashion, and it further introduces an alternative solution for the objective function optimization. Extensive experimental results show the effectiveness of WSM3L on benchmark datasets.

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.

scholarly journals An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers

2019 ◽  
Vol 22 (64) ◽  
pp. 63-84
Author(s):  
JanapatyI Naga Muneiah ◽  
Ch D V SubbaRao
Keyword(s):  
Decision Tree ◽  
Probability Estimation ◽  
Automatic Extraction ◽  
Business Applications ◽  
Computational Performance ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Phone Service ◽  
The Cost ◽  
Service Data

Enterprises often classify their customers based on the degree of profitability in decreasing order like C1, C2, ..., Cn. Generally, customers representing class Cn are zero profitable since they migrate to the competitor. They are called as attritors (or churners) and are the prime reason for the huge losses of the enterprises. Nevertheless, customers of other intermediary classes are reluctant and offer an insignificant amount of profits in different degrees and lead to uncertainty. Various data mining models like decision trees, etc., which are built using the customers’ profiles, are limited to classifying the customers as attritors or non-attritors only and not providing profitable actionable knowledge. In this paper, we present an efficient algorithm for the automatic extraction of profit-maximizing knowledge for business applications with multi-class customers by postprocessing the probability estimation decision tree (PET). When the PET predicts a customer as belonging  to any of the lesser profitable classes, then, our algorithm suggests the cost-sensitive actions to change her/him to a maximum possible higher profitable status. In the proposed novel approach, the PET is represented in the compressed form as a Bit patterns matrix and the postprocessing task is performed on the bit patterns by applying the bitwise AND operations. The computational performance of the proposed method is strong due to the employment of effective data structures. Substantial experiments conducted on UCI datasets, real Mobile phone service data and other benchmark datasets demonstrate that the proposed method remarkably outperforms the state-of-the-art methods.

scholarly journals DIMENSIONALITY REDUCTION VIA AN ORTHOGONAL AUTOENCODER APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 357-362
Author(s):  
V. H. Ayma ◽  
V. A. Ayma ◽  
J. Gutierrez
Keyword(s):  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Original Data ◽  
Data Representation ◽  
Hyperspectral Data ◽  
Attractive Alternative ◽  
Kennedy Space Center ◽  
Classification Tasks ◽  
Multiple Levels ◽  
Kennedy Space

Abstract. Nowadays, the increasing amount of information provided by hyperspectral sensors requires optimal solutions to ease the subsequent analysis of the produced data. A common issue in this matter relates to the hyperspectral data representation for classification tasks. Existing approaches address the data representation problem by performing a dimensionality reduction over the original data. However, mining complementary features that reduce the redundancy from the multiple levels of hyperspectral images remains challenging. Thus, exploiting the representation power of neural networks based techniques becomes an attractive alternative in this matter. In this work, we propose a novel dimensionality reduction implementation for hyperspectral imaging based on autoencoders, ensuring the orthogonality among features to reduce the redundancy in hyperspectral data. The experiments conducted on the Pavia University, the Kennedy Space Center, and Botswana hyperspectral datasets evidence such representation power of our approach, leading to better classification performances compared to traditional hyperspectral dimensionality reduction algorithms.

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