scholarly journals Latent Semantics Encoding for Label Distribution Learning

2019 ◽  
Author(s):  
Suping Xu ◽  
Lin Shang ◽  
Furao Shen
Keyword(s):  
Empirical Studies ◽  
Semantic Space ◽  
Learning Ability ◽  
Semantic Features ◽  
Risk Minimization ◽  
Negative Effects ◽  
Real World Data ◽  
Empirical Risk ◽  
Label Distribution Learning ◽  
Label Distribution

Label distribution learning (LDL) is a newly arisen learning paradigm to deal with label ambiguity problems, which can explore the relative importance of different labels in the description of a particular instance. Although some existing LDL algorithms have achieved better effectiveness in real applications, most of them typically emphasize on improving the learning ability by manipulating the label space, while ignoring the fact that irrelevant and redundant features exist in most practical classification learning tasks, which increase not only storage requirements but also computational overheads. Furthermore, noises in data acquisition will bring negative effects on the generalization performance of LDL algorithms. In this paper, we propose a novel algorithm, i.e., Latent Semantics Encoding for Label Distribution Learning (LSE-LDL), which learns the label distribution and implements feature selection simultaneously under the guidance of latent semantics. Specifically, to alleviate noise disturbances, we seek and encode discriminative original physical/chemical features into advanced latent semantic features, and then construct a mapping from the encoded semantic space to the label space via empirical risk minimization. Empirical studies on 15 real-world data sets validate the effectiveness of the proposed algorithm.

scholarly journals Generalization Bounds Derived IPM-Based Regularization for Domain Adaptation

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Juan Meng ◽  
Guyu Hu ◽  
Dong Li ◽  
Yanyan Zhang ◽  
Zhisong Pan
Keyword(s):  
Domain Adaptation ◽  
Empirical Studies ◽  
Synthetic Data ◽  
Risk Minimization ◽  
Target Domain ◽  
Real World Data ◽  
Source Domain ◽  
Generalization Bounds ◽  
Empirical Risk ◽  
Testing Error

Domain adaptation has received much attention as a major form of transfer learning. One issue that should be considered in domain adaptation is the gap between source domain and target domain. In order to improve the generalization ability of domain adaption methods, we proposed a framework for domain adaptation combining source and target data, with a new regularizer which takes generalization bounds into account. This regularization term considers integral probability metric (IPM) as the distance between the source domain and the target domain and thus can bound up the testing error of an existing predictor from the formula. Since the computation of IPM only involves two distributions, this generalization term is independent with specific classifiers. With popular learning models, the empirical risk minimization is expressed as a general convex optimization problem and thus can be solved effectively by existing tools. Empirical studies on synthetic data for regression and real-world data for classification show the effectiveness of this method.

Constrained ERM Learning of Canonical Correlation Analysis: A Least Squares Perspective

2017 ◽  
Vol 29 (10) ◽  
pp. 2825-2859 ◽  
Author(s):  
Jia Cai ◽  
Hongwei Sun
Keyword(s):  
Correlation Analysis ◽  
Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Data Sets ◽  
Risk Minimization ◽  
Real World Data ◽  
Empirical Risk ◽  
Statistical Consistency ◽  
Kaczmarz Method

Canonical correlation analysis (CCA) is a useful tool in detecting the latent relationship between two sets of multivariate variables. In theoretical analysis of CCA, a regularization technique is utilized to investigate the consistency of its analysis. This letter addresses the consistency property of CCA from a least squares view. We construct a constrained empirical risk minimization framework of CCA and apply a two-stage randomized Kaczmarz method to solve it. In the first stage, we remove the noise, and in the second stage, we compute the canonical weight vectors. Rigorous theoretical consistency is addressed. The statistical consistency of this novel scenario is extended to the kernel version of it. Moreover, experiments on both synthetic and real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithms.

scholarly journals Label Distribution Learning with Label Correlations via Low-Rank Approximation

2019 ◽  
Author(s):  
Tingting Ren ◽  
Xiuyi Jia ◽  
Weiwei Li ◽  
Shu Zhao
Keyword(s):  
Correlation Matrix ◽  
Learning Algorithm ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Real World Data ◽  
Label Correlations ◽  
Rank Approximation ◽  
Label Distribution Learning ◽  
Label Distribution ◽  
Label Correlation

Label distribution learning (LDL) can be viewed as the generalization of multi-label learning. This novel paradigm focuses on the relative importance of different labels to a particular instance. Most previous LDL methods either ignore the correlation among labels, or only exploit the label correlations in a global way. In this paper, we utilize both the global and local relevance among labels to provide more information for training model and propose a novel label distribution learning algorithm. In particular, a label correlation matrix based on low-rank approximation is applied to capture the global label correlations. In addition, the label correlation among local samples are adopted to modify the label correlation matrix. The experimental results on real-world data sets show that the proposed algorithm outperforms state-of-the-art LDL methods.

scholarly journals Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization

2017 ◽  
Author(s):  
Zebang Shen ◽  
Hui Qian ◽  
Tongzhou Mu ◽  
Chao Zhang
Keyword(s):  
Large Scale ◽  
Empirical Studies ◽  
Accurate Solution ◽  
Gradient Algorithm ◽  
Stochastic Algorithm ◽  
Empirical Risk Minimization ◽  
Risk Minimization ◽  
Doubly Stochastic ◽  
Empirical Risk ◽  
Convex Case

Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications. In this paper, we propose a doubly stochastic algorithm with a novel accelerating multi-momentum technique to solve large scale empirical risk minimization problem for learning tasks. While enjoying a provably superior convergence rate, in each iteration, such algorithm only accesses a mini batch of samples and meanwhile updates a small block of variable coordinates, which substantially reduces the amount of memory reference when both the massive sample size and ultra-high dimensionality are involved. Specifically, to obtain an ε-accurate solution, our algorithm requires only O(log(1/ε)/sqrt(ε)) overall computation for the general convex case and O((n+sqrt{nκ})log(1/ε)) for the strongly convex case. Empirical studies on huge scale datasets are conducted to illustrate the efficiency of our method in practice.

scholarly journals Label distribution learning with label-specific features

2019 ◽  
Author(s):  
Tingting Ren ◽  
Xiuyi Jia ◽  
Weiwei Li ◽  
Lei Chen ◽  
Zechao Li
Keyword(s):  
Feature Space ◽  
Data Representation ◽  
Data Sets ◽  
Real World Data ◽  
Feature Selection Problem ◽  
Label Correlations ◽  
The Common ◽  
Label Distribution Learning ◽  
Class Labels ◽  
Label Distribution

Label distribution learning (LDL) is a novel machine learning paradigm to deal with label ambiguity issues by placing more emphasis on how relevant each label is to a particular instance. Many LDL algorithms have been proposed and most of them concentrate on the learning models, while few of them focus on the feature selection problem. All existing LDL models are built on a simple feature space in which all features are shared by all the class labels. However, this kind of traditional data representation strategy tends to select features that are distinguishable for all labels, but ignores label-specific features that are pertinent and discriminative for each class label. In this paper, we propose a novel LDL algorithm by leveraging label-specific features. The common features for all labels and specific features for each label are simultaneously learned to enhance the LDL model. Moreover, we also exploit the label correlations in the proposed LDL model. The experimental results on several real-world data sets validate the effectiveness of our method.

scholarly journals Determinants of Corporate Anti-Corruption Disclosure: The Case of the Emerging Economics

2021 ◽  
Vol 13 (6) ◽  
pp. 3462
Author(s):  
Maider Aldaz Odriozola ◽  
Igor Álvarez Etxeberria
Keyword(s):  
Empirical Studies ◽  
Geographical Area ◽  
Control Variable ◽  
Emerging Countries ◽  
Point Of View ◽  
Press Freedom ◽  
Negative Effects ◽  
Key Factor ◽  
Using Data ◽  
Positive Effect

Corruption is a key factor that affects countries’ development, with emerging countries being a geographical area in which it tends to generate greater negative effects. However, few empirical studies analyze corruption from the point of view of disclosure by companies in this relevant geographical area. Based on a regression analysis using data from the 96 large companies from 15 emerging countries included in the 2016 International Transparency Report, this paper seeks to understand what determinants affect such disclosure. In that context, this paper provides empirical evidence to understand the factors that influence reporting on anti-corruption mechanisms in an area of high economic importance that has been little studied to date, pointing to the positive effect of press freedom in a country where the company is located and with the industry being the unique control variable that strengthens this relationship.

Active label distribution learning

2021 ◽  
Vol 436 ◽  
pp. 12-21
Author(s):  
Xinyue Dong ◽  
Shilin Gu ◽  
Wenzhang Zhuge ◽  
Tingjin Luo ◽  
Chenping Hou
Keyword(s):  
Label Distribution Learning ◽  
Label Distribution

scholarly journals Perception of Gamblers: A Systematic Review

2021 ◽  
Author(s):  
Andrea Wöhr ◽  
Marius Wuketich
Keyword(s):  
Systematic Review ◽  
Empirical Studies ◽  
Grey Literature ◽  
Future Research ◽  
Gambling Problems ◽  
Negative Effects ◽  
Strong Focus ◽  
The Media ◽  
Social Scientific ◽  
Psychological Knowledge

AbstractIt is generally assumed that gamblers, and particularly people with gambling problems (PG), are affected by negative perception and stigmatisation. However, a systematic review of empirical studies investigating the perception of gamblers has not yet been carried out. This article therefore summarises empirical evidence on the perception of gamblers and provides directions for future research. A systematic literature review based on the relevant guidelines was carried out searching three databases. The databases Scopus, PubMed and BASE were used to cover social scientific knowledge, medical-psychological knowledge and grey literature. A total of 48 studies from 37 literature references was found. The perspective in these studies varies: Several studies focus on the perception of gamblers by the general population, by subpopulations (e. g. students or social workers), or by gamblers on themselves. The perspective on recreational gamblers is hardly an issue. A strong focus on persons with gambling problems is symptomatic of the gambling discourse. The analysis of the studies shows that gambling problems are thought to be rather concealable, whereas the negative effects on the concerned persons‘ lives are rated to be quite substantial. PG are described as “irresponsible” and “greedy” while they perceive themselves as “stupid” or “weak”. Only few examples of open discrimination are mentioned. Several studies however put emphasis on the stereotypical way in which PG are portrayed in the media, thus contributing to stigmatisation. Knowledge gaps include insights from longitudinal studies, the influence of respondents‘ age, culture and sex on their views, the relevance of the type of gambling a person is addicted to, and others. Further studies in these fields are needed.

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