scholarly journals LSV-Based Tail Inequalities for Sums of Random Matrices

2017 ◽  
Vol 29 (1) ◽  
pp. 247-262 ◽  
Author(s):  
Chao Zhang ◽  
Lei Du ◽  
Dacheng Tao
Keyword(s):  
Machine Learning ◽  
Laplace Transform ◽  
Random Matrices ◽  
Singular Value ◽  
Diagonal Matrix ◽  
New Method ◽  
Learning Models ◽  
The Matrix ◽  
Diagonalization Method ◽  
Machine Learning Models

The techniques of random matrices have played an important role in many machine learning models. In this letter, we present a new method to study the tail inequalities for sums of random matrices. Different from other work (Ahlswede & Winter, 2002 ; Tropp, 2012 ; Hsu, Kakade, & Zhang, 2012 ), our tail results are based on the largest singular value (LSV) and independent of the matrix dimension. Since the LSV operation and the expectation are noncommutative, we introduce a diagonalization method to convert the LSV operation into the trace operation of an infinitely dimensional diagonal matrix. In this way, we obtain another version of Laplace-transform bounds and then achieve the LSV-based tail inequalities for sums of random matrices.

A dependency-based machine learning approach to the identification of research topics: a case in COVID-19 studies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Haoran Zhu ◽  
Lei Lei
Keyword(s):  
Machine Learning ◽  
Noun Phrases ◽  
New Method ◽  
Learning Models ◽  
Linguistic Features ◽  
Research Topics ◽  
Content Type ◽  
Research Areas ◽  
Information Retrieval Methods ◽  
Machine Learning Models

PurposePrevious research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics.Design/methodology/approachFirst, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity.FindingsThe new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results.Originality/valueThis study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.

scholarly journals Contextualized LORE for Fuzzy Attributes

2021 ◽  
Author(s):  
Najlaa Maaroof ◽  
Antonio Moreno ◽  
Mohammed Jabreel ◽  
Aida Valls
Keyword(s):  
Machine Learning ◽  
Contextual Information ◽  
Black Box ◽  
New Method ◽  
Learning Models ◽  
Box Models ◽  
Black Boxes ◽  
Linguistic Labels ◽  
Black Box Models ◽  
Machine Learning Models

Despite the broad adoption of Machine Learning models in many domains, they remain mostly black boxes. There is a pressing need to ensure Machine Learning models that are interpretable, so that designers and users can understand the reasons behind their predictions. In this work, we propose a new method called C-LORE-F to explain the decisions of fuzzy-based black box models. This new method uses some contextual information about the attributes as well as the knowledge of the fuzzy sets associated to the linguistic labels of the fuzzy attributes to provide actionable explanations. The experimental results on three datasets reveal the effectiveness of C-LORE-F when compared with the most relevant related works.

scholarly journals Expanding WordNet with Gloss and Polysemy Links for Evocation Strength Recognition

2020 ◽  
Author(s):  
Marek Maziarz ◽  
Ewa Rudnicka
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Relations ◽  
New Method ◽  
Learning Models ◽  
Dijkstra's Algorithm ◽  
Vector Representations ◽  
Machine Learning Models

Expanding WordNet with Gloss and Polysemy Links for Evocation Strength RecognitionEvocation – a phenomenon of sense associations going beyond standard (lexico)-semantic relations – is difficult to recognise for natural language processing systems. Machine learning models give predictions which are only moderately correlated with the evocation strength. It is believed that ordinary graph measures are not as good at this task as methods based on vector representations. The paper proposes a new method of enriching the WordNet structure with weighted polysemy and gloss links, and proves that Dijkstra’s algorithm performs equally as well as other more sophisticated measures when set together with such expanded structures. Rozszerzenie WordNetu o glosy i relacje polisemiczne na potrzeby rozpoznawania siły ewokacjiEwokacja – zjawisko skojarzeń zmysłowych wykraczających poza standardowe (leksykalne) relacje semantyczne – jest trudne do rozpoznania dla systemów przetwarzania języka naturalnego. Modele uczenia maszynowego dają prognozy tylko umiarkowanie skorelowane z siłą ewokacji. Uważa się, że zwykłe miary grafowe nie są tak dobre w tym zadaniu, jak metody oparte na reprezentacjach wektorowych. Proponujemy nową metodę wzbogacania struktury WordNet o polisemie ważone i linki połysku i udowadniamy, że algorytm Dijkstry zestawiony z tak rozbudowanymi strukturami działa a także inne, bardziej wyrafinowane środki.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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