What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Carlotta Domeniconi mainly investigates Artificial intelligence, Pattern recognition, Data mining, Cluster analysis and Machine learning.
Artificial intelligence is frequently linked to Set in her study.
The concepts of her Pattern recognition study are interwoven with issues in Clustering high-dimensional data and Curse of dimensionality.
Her Data mining study combines topics from a wide range of disciplines, such as Estimator and Kernel density estimation.
Her Cluster analysis research integrates issues from Intrusion detection system, Transduction and Outlier.
Her study in Machine learning is interdisciplinary in nature, drawing from both Topic model, Latent Dirichlet allocation, Protein function prediction and Benchmark.
Her most cited work include:
- On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking (341 citations)
- Locally adaptive metric nearest-neighbor classification (277 citations)
- Locally adaptive metrics for clustering high dimensional data (200 citations)
What are the main themes of her work throughout her whole career to date?
Carlotta Domeniconi spends much of her time researching Artificial intelligence, Cluster analysis, Data mining, Machine learning and Pattern recognition.
Her Artificial intelligence research incorporates themes from Protein function prediction, Set and Natural language processing.
Her work deals with themes such as Ranking, Supervised learning and Outlier, which intersect with Data mining.
The study incorporates disciplines such as Crowdsourcing, Latent Dirichlet allocation, Topic model and Robustness in addition to Machine learning.
Her k-nearest neighbors algorithm and Dimensionality reduction study in the realm of Pattern recognition connects with subjects such as Linear subspace.
Her studies deal with areas such as Nearest neighbor search and Nearest-neighbor chain algorithm as well as k-nearest neighbors algorithm.
She most often published in these fields:
- Artificial intelligence (53.23%)
- Cluster analysis (32.26%)
- Data mining (31.72%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (53.23%)
- Data mining (31.72%)
- Cluster analysis (32.26%)
In recent papers she was focusing on the following fields of study:
Carlotta Domeniconi mainly investigates Artificial intelligence, Data mining, Cluster analysis, Matrix decomposition and Machine learning.
Her research combines Pattern recognition and Artificial intelligence.
As a part of the same scientific study, Carlotta Domeniconi usually deals with the Pattern recognition, concentrating on Semantic similarity and frequently concerns with Multi-label classification, Zero shot learning and Word embedding.
Her research in the fields of Relation overlaps with other disciplines such as Redundancy.
Her study in Cluster analysis is interdisciplinary in nature, drawing from both Data point, Rule of thumb, Reduction and Data science.
Her Machine learning study integrates concerns from other disciplines, such as Crowdsourcing and Robustness.
Between 2018 and 2021, her most popular works were:
- Ranking-Based Deep Cross-Modal Hashing (13 citations)
- Isoform function prediction based on bi-random walks on a heterogeneous network (11 citations)
- Multi-View Multiple Clusterings using Deep Matrix Factorization (10 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Carlotta Domeniconi focuses on Matrix decomposition, Data mining, Heterogeneous network, Cluster analysis and Relational database.
Her Data mining research is multidisciplinary, relying on both Ranking and Hash function.
Her studies link Data science with Cluster analysis.
Her Relational database study incorporates themes from Sensor fusion and Disease Association.
The Embedding study combines topics in areas such as Machine learning, Discriminative model and Convolutional neural network.
Her Discriminative model study results in a more complete grasp of Artificial intelligence.
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