Relevant bibliographies by topics / T-distributed Stochastic Neighbour Embedding

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'T-distributed Stochastic Neighbour Embedding'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "T-distributed Stochastic Neighbour Embedding"

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Rice, Iain. "Γ-stochastic neighbour embedding for feed-forward data visualization". Information Visualization 17, № 4 (2017): 306–15. http://dx.doi.org/10.1177/1473871617715212.

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t-Distributed stochastic neighbour embedding is one of the most popular non-linear dimension-reduction techniques used in multiple application domains. In this article, we propose a variation on the embedding neighbourhood distribution, resulting in Γ-stochastic neighbour embedding, which can construct a feed-forward mapping using a radial basis function network. We compare the visualizations generated by Γ-stochastic neighbour embedding with those of t-distributed stochastic neighbour embedding and provide empirical evidence suggesting the network is capable of robust interpolation and automatic weight regularization.
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Sudha, T., and P. Nagendra Kumar. "Performance Analysis of Dimensionality Reduction Techniques in the Context of Clustering." Asian Journal of Computer Science and Technology 8, S3 (2019): 66–71. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2084.

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Data mining is one of the major areas of research. Clustering is one of the main functionalities of datamining. High dimensionality is one of the main issues of clustering and Dimensionality reduction can be used as a solution to this problem. The present work makes a comparative study of dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis in the context of clustering. High dimensional data have been reduced to low dimensional data using dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis. Cluster analysis has been performed on the high dimensional data as well as the low dimensional data sets obtained through t-distributed stochastic neighbour embedding and Probabilistic principal component analysis with varying number of clusters. Mean squared error; time and space have been considered as parameters for comparison. The results obtained show that time taken to convert the high dimensional data into low dimensional data using probabilistic principal component analysis is higher than the time taken to convert the high dimensional data into low dimensional data using t-distributed stochastic neighbour embedding.The space required by the data set reduced through Probabilistic principal component analysis is less than the storage space required by the data set reduced through t-distributed stochastic neighbour embedding.
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Chan, David M., Roshan Rao, Forrest Huang, and John F. Canny. "GPU accelerated t-distributed stochastic neighbor embedding." Journal of Parallel and Distributed Computing 131 (September 2019): 1–13. http://dx.doi.org/10.1016/j.jpdc.2019.04.008.

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Abimanyu, Satria, Nurdin Bahtiar, and Eko Adi Sarwoko. "Implementasi Metode Support Vector Machine (SVM) dan t-Distributed Stochastic Neighbor Embedding (t-SNE) untuk Klasifikasi Depresi." JURNAL MASYARAKAT INFORMATIKA 14, no. 2 (2023): 146–58. http://dx.doi.org/10.14710/jmasif.14.2.59513.

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Depresi merupakan salah satu gangguan kesehatan mental. Sekitar 300 juta jiwa atau 3,76% populasi di dunia dari segala usia dan komunitas menderita depresi. WHO memprediksi bahwa depresi akan menjadi penyebab kematian paling berdampak dalam 15 tahun ke depan. Penelitian terdahulu yang melakukan klasifikasi terhadap depresi untuk instrumen Depression Anxiety Stress Scales (DASS-42) masih sangat sedikit. Penelitian ini mengidentifikasi seseorang memiliki kemungkinan depresi, melalui proses pelatihan model klasifikasi menggunakan metode Support Vector Machine dan t-Distributed Stochastic Neighbor Embedding pada set data DASS-42. Set data DASS-42 terdiri dari 39.776 data dan dapat digunakan untuk mengklasifikasi 3 fenomena yang berbeda yaitu, depresi, stress dan kecemasan. Model Support Vector Machine dilatih menggunakan data DASS-42 yang telah dibersihkan, normalisasi dan balancing serta menggunakan atribut yang telah direduksi melalui proses reduksi dimensi t-Distributed Stochastic Neighbor Embedding. Data latih dan data uji dibagi dengan rasio 80:20. Berdasarkan hasil pengujian, implementasi metode Support Vector Machine (SVM) dan t-Distributed Stochastic Neighbor Embedding (t-SNE) untuk klasifikasi depresi pada data DASS-42 menunjukkan performa yang lebih baik dengan akurasi terbaik sebesar 100% pada data sebelum balancing dan 91,71% pada data setelah balancing.
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Valente, Daria, Chiara De Gregorio, Valeria Torti, et al. "Finding Meanings in Low Dimensional Structures: Stochastic Neighbor Embedding Applied to the Analysis of Indri indri Vocal Repertoire." Animals 9, no. 5 (2019): 243. http://dx.doi.org/10.3390/ani9050243.

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Although there is a growing number of researches focusing on acoustic communication, the lack of shared analytic approaches leads to inconsistency among studies. Here, we introduced a computational method used to examine 3360 calls recorded from wild indris (Indri indri) from 2005–2018. We split each sound into ten portions of equal length and, from each portion we extracted spectral coefficients, considering frequency values up to 15,000 Hz. We submitted the set of acoustic features first to a t-distributed stochastic neighbor embedding algorithm, then to a hard-clustering procedure using a k-means algorithm. The t-distributed stochastic neighbor embedding (t-SNE) mapping indicated the presence of eight different groups, consistent with the acoustic structure of the a priori identification of calls, while the cluster analysis revealed that an overlay between distinct call types might exist. Our results indicated that the t-distributed stochastic neighbor embedding (t-SNE), successfully been employed in several studies, showed a good performance also in the analysis of indris’ repertoire and may open new perspectives towards the achievement of shared methodical techniques for the comparison of animal vocal repertoires.
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Zhang, Haili, Pu Wang, Xuejin Gao, Yongsheng Qi, and Huihui Gao. "Process Data Visualization Using Bikernel t-Distributed Stochastic Neighbor Embedding." Industrial & Engineering Chemistry Research 59, no. 44 (2020): 19623–32. http://dx.doi.org/10.1021/acs.iecr.0c03333.

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Wang, Jing, Xiaobin Cheng, Xun Wang, et al. "On the solidification of the manifold of the t-distributed stochastic neighbour embedding for condition classification of machine tools." Engineering Research Express 3, no. 4 (2021): 045031. http://dx.doi.org/10.1088/2631-8695/ac37f0.

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Abstract t-distributed stochastic neighbour embedding (t-SNE) is of considerable interest in machining condition monitoring for feature selection. In this paper, the neural networks are introduced to solidify the manifold of the t-SNE prior to classification. This leads to the improved feature selection method, namely the Net-SNE. Conventional statistical features are first extracted from vibration signals to form a high dimensional feature vector. The redundancies in the feature vector are subsequently removed by the t-SNE. Then the neural networks build a mapping model between the high dimensional feature vector and the selected features. The new data is calculated directly using the mapping model. The experiments were conducted on a lathe and a milling machine to collect vibration signals under common working conditions. The K-nearest neighbour classifier is applied to a small sample case and a class-imbalance case to compare the classification performance with and without the Net-SNE. The results demonstrate that the Net-SNE has the advantage over the t-SNE, since it can mine the discriminative features and solidifiy the manifold in the calculation of the new data. Moreover, the proposed method significantly improves the classification accuracy by Net-SNE, along with better classification performance in data-limited situations.
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Kim, Kipoong, and Choongrak Kim. "A review on the t-distributed stochastic neighbors embedding." Korean Journal of Applied Statistics 36, no. 2 (2023): 167–73. http://dx.doi.org/10.5351/kjas.2023.36.2.167.

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Abbas, Ahmed Khudhair, Adil Ibrahim Khalil, and Sameera A. Abdulkader. "Social Touch Recognition Based on Support Vector Machine and T-Distributed Stochastic Neighbour Embedding as Pre-processing." IOP Conference Series: Materials Science and Engineering 1076, no. 1 (2021): 012042. http://dx.doi.org/10.1088/1757-899x/1076/1/012042.

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Urrutia, Robin, Diego Espejo, Natalia Evens, et al. "Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions." Sensors 23, no. 23 (2023): 9297. http://dx.doi.org/10.3390/s23239297.

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This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.
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Dissertations / Theses on the topic "T-distributed Stochastic Neighbour Embedding"

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Droh, Erik. "T-Distributed Stochastic Neighbor Embedding Data Preprocessing Impact on Image Classification using Deep Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237422.

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Image classification in Machine Learning encompasses the task of identification of objects in an image. The technique has applications in various areas such as e-commerce, social media and security surveillance. In this report the author explores the impact of using t-Distributed Stochastic Neighbor Embedding (t-SNE) on data as a preprocessing step when classifying multiple classes of clothing with a state-of-the-art Deep Convolutional Neural Network (DCNN). The t-SNE algorithm uses dimensionality reduction and groups similar objects close to each other in three-dimensional space. Extracting this information in the form of a positional coordinate gives us a new parameter which could help with the classification process since the features it uses can be different from that of the DCNN. Therefore, three slightly different DCNN models receives different input and are compared. The first benchmark model only receives pixel values, the second and third receive pixel values together with the positional coordinates from the t-SNE preprocessing for each data point, but with different hyperparameter values in the preprocessing step. The Fashion-MNIST dataset used contains 10 different clothing classes which are normalized and gray-scaled for easeof-use. The dataset contains 70.000 images in total. Results show minimum change in classification accuracy in the case of using a low-density map with higher learning rate as the data size increases, while a more dense map and lower learning rate performs a significant increase in accuracy of 4.4% when using a small data set. This is evidence for the fact that the method can be used to boost results when data is limited.<br>Bildklassificering i maskinlärning innefattar uppgiften att identifiera objekt i en bild. Tekniken har applikationer inom olika områden så som e-handel, sociala medier och säkerhetsövervakning. I denna rapport undersöker författaren effekten av att användat-Distributed Stochastic Neighbour Embedding (t-SNE) på data som ett förbehandlingssteg vid klassificering av flera klasser av kläder med ett state-of-the-art Deep Convolutio-nal Neural Network (DCNN). t-SNE-algoritmen använder dimensioneringsreduktion och grupperar liknande objekt nära varandra i tredimensionellt utrymme. Att extrahera denna information i form av en positionskoordinat ger oss en ny parameter som kan hjälpa till med klassificeringsprocessen eftersom funktionerna som den använder kan skilja sig från DCNN-modelen. Tre olika DCNN-modeller får olika in-data och jämförs därefter. Den första referensmodellen mottar endast pixelvärden, det andra och det tredje motar pixelvärden tillsammans med positionskoordinaterna från t-SNE-förbehandlingen för varje datapunkt men med olika hyperparametervärden i förbehandlingssteget. I studien används Fashion-MNIST datasetet som innehåller 10 olika klädklasser som är normaliserade och gråskalade för enkel användning. Datasetet innehåller totalt 70.000 bilder. Resultaten visar minst förändring i klassificeringsnoggrannheten vid användning av en låg densitets karta med högre inlärningsgrad allt eftersom datastorleken ökar, medan en mer tät karta och lägre inlärningsgrad uppnår en signifikant ökad noggrannhet på 4.4% när man använder en liten datamängd. Detta är bevis på att metoden kan användas för att öka klassificeringsresultaten när datamängden är begränsad.
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Agis, Cherta David. "Desarrollo de un sistema de monitorización de la integridad estructural para aplicaciones en ingeniería mediante técnicas de reducción de la dimensionalidad." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/670561.

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This thesis describes a structural health monitoring (SHM) strategy to detect and classify changes in structures that can be equipped with sensors. SHM is an area of great interest, because its main objective is to verify the health of the structure to ensure its correct operation and, in turn, save maintenance costs. This objective is achieved by using algorithms and equipping the structure with a network of sensors that continuously monitor it. Researchers from around the world focus their efforts on the development of new forms of continuous monitoring to know the current state of the structure and to avoid possible failures or catastrophes. In this sense, in this work, a network of piezoelectric sensors (PZTs) is used for the development of the strategy of detection and classification of structural changes. This network of PZTs, attached to the surface of the structure to be diagnosed, applies vibrational excitation signals and, at the same time, collects the responses propagated through the structure. With this collected information, certain mathematical algorithms are developed. To carry out the main task of the proposed methodology, detection and classification of structural changes, the technique called t-distributed stochastic neighbor embedding (t-SNE) is essentially used. This technique is capable of representing the local structure of the high-dimensional data collected by the sensor network in two-dimensional or three-dimensional space. Furthermore, for the classification of structural changes, the detection methodology is expanded by adding the use of three strategies: (a) the smallest point-centroid distance; (b) the majority vote; and (c) the sum of the inverses of the distances. The methodology proposed in this study is tested and validated using an aluminum plate equipped with four PZT sensors and for certain predefined structural changes. The promising results obtained show the great classification capacity and the strong performance of this methodology, successfully classifying about 100% of the cases in various experimental scenarios. The main contribution of this project is the combination of the t-SNE technique with a carefully selected pre-processing of the data and with the three proposed classification strategies. This combination significantly improves the quality of the groups or clusters obtained with the damage detection and classification method, which represent the different structural states. Likewise, said combination diagnoses a structure with a low computational cost and high reliability. Regarding the applicability of the suggested strategy, there is no prescribed field of application: if a network of sensors can be installed in the structure to be diagnosed and several phases of action can be considered, the approach presented here can be, a priori, implemented.<br>Esta tesis describe una estrategia de monitorización de la salud estructural (SHM, por sus siglas en inglés) para detectar y clasificar fallos en estructuras que pueden ser equipadas con sensores. La SHM es un área de gran interés, ya que su objetivo principal es la verificación de la salud de la estructura para asegurar su correcto funcionamiento y, a su vez, ahorrar costes de mantenimiento. Este objetivo se consigue haciendo uso de algoritmos y equipando a la estructura con una red de sensores que la monitorizan de forma continuada. Investigadores de todo el mundo centran sus esfuerzos en el desarrollo de nuevas formas de monitorización continua para conocer el estado actual de la estructura y evitar posibles fallos o catástrofes. En este sentido, en este trabajo, se utiliza una red de sensores piezoeléctricos (PZT, por sus siglas en inglés) para el desarrollo de la estrategia de detección y clasificación de los cambios estructurales. Esta red de PZT, adherida a la superficie de la estructura a diagnosticar, aplica señales vibracionales de excitación y al mismo tiempo recoge las respuestas propagadas a través de la estructura. Con esta información recopilada se desarrollan ciertos algoritmos matemáticos. Para llevar a cabo la tarea principal de la metodología propuesta, detección y clasificación de fallos, se utiliza esencialmente la técnica denominada t-distributed stochastic neighbor embedding (t-SNE). Dicha técnica es capaz de representar la estructura local de los datos de alta dimensionalidad recopilados por la red de sensores en un espacio bidimensional o tridimensional. Además, para la clasificación de los cambios estructurales, se amplía la metodología de detección añadiendo el uso de tres estrategias: (a) la distancia punto-centroide más pequeña; (b) el voto mayoritario; y (c) la suma de las inversas de las distancias. La metodología propuesta en este estudio se prueba y valida utilizando una placa de aluminio equipada con cuatro sensores PZT y para ciertos daños predefinidos. Los prometedores resultados obtenidos ponen de manifiesto la gran capacidad de clasificación y el fuerte rendimiento de esta metodología, clasificando con éxito cerca del 100% de los casos en varios escenarios experimentales. La principal contribución de este proyecto es la combinación de la técnica t-SNE con un preprocesamiento de los datos cuidosamente seleccionado y con las tres estrategias de clasificación propuestas. Esta combinación mejora significativamente la calidad de los grupos o clústeres obtenidos con el método de detección y clasificación de daños, que representan los diferentes estados estructurales. Asimismo, dicha combinación diagnostica una estructura con un bajo coste computacional y una alta fiabilidad. En cuanto a la aplicabilidad de la estrategia sugerida, no hay un campo de aplicación prescrito: si se puede instalar una red de sensores en la estructura a diagnosticar y se pueden considerar varias fases de actuación, el enfoque aquí presentado puede implementarse a priori.
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Book chapters on the topic "T-distributed Stochastic Neighbour Embedding"

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Balamurali, Mehala. "t-Distributed Stochastic Neighbor Embedding." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_446.

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Balamurali, Mehala. "t-Distributed Stochastic Neighbor Embedding." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_446-1.

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Tripathy, B. K., S. Anveshrithaa, and Shrusti Ghela. "t-Distributed Stochastic Neighbor Embedding (t-SNE)." In Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization. CRC Press, 2021. http://dx.doi.org/10.1201/9781003190554-13.

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Nápoles, Gonzalo, Leonardo Concepción, Büşra Özgöde Yigin, Görkem Saygili, Koen Vanhoof, and Rafael Bello. "Weighted t-Distributed Stochastic Neighbor Embedding for Projection-Based Clustering." In Progress in Artificial Intelligence and Pattern Recognition. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49552-6_12.

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Kitazono, Jun, Nistor Grozavu, Nicoleta Rogovschi, Toshiaki Omori, and Seiichi Ozawa. "t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46675-0_14.

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Fooladgar, E., and C. Duwig. "Identification of Combustion Trajectories Using t-Distributed Stochastic Neighbor Embedding (t-SNE)." In Direct and Large-Eddy Simulation XI. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04915-7_33.

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Soni, Jayesh, Nagarajan Prabakar, and Himanshu Upadhyay. "Visualizing High-Dimensional Data Using t-Distributed Stochastic Neighbor Embedding Algorithm." In Principles of Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43981-1_9.

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Lee, John A., and Michel Verleysen. "On the Role and Impact of the Metaparameters in t-distributed Stochastic Neighbor Embedding." In Proceedings of COMPSTAT'2010. Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_31.

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Meniailov, Ievgen, Serhii Krivtsov, and Tetyana Chumachenko. "Dimensionality Reduction of Diabetes Mellitus Patient Data Using the T-Distributed Stochastic Neighbor Embedding." In Smart Technologies in Urban Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20141-7_9.

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Mesai Belgacem, Ahmed, Mounir Hadef, and Abdesslem Djerdir. "Fault Diagnosis of Photovoltaic Arrays Based on Support Vector Machine and t-Distributed Stochastic Neighbor Embedding." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4776-4_18.

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Conference papers on the topic "T-distributed Stochastic Neighbour Embedding"

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Nassar, Sara Husam, Samir Belhaouari, Mebarka Allaoui, and Mohammed Lamine Kherfi. "Centroid Initialization Method for t-Distributed Stochastic Neighbour Embedding (t-SNE)." In 2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP). IEEE, 2024. https://doi.org/10.1109/mlnlp63328.2024.10800182.

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D, Bujji Babu, Venkateswarlu B, Tamilselvan S, Pravin Balu B, Balasundaram N, and Praveenkumar R. "Outlier Detection in High-Dimensional Data using t-Distributed Stochastic Neighbor Embedding." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011342.

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P, Chandra Sekar, Vasanthakumar G U, Myasar M. Adnan, Thota Soujanya, and Hafidh I. Ai Sadi. "Network Traffic Anomaly Detection Using t-Distributed Stochastic Neighbor Embedding and Variational Autoencoder Approach." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721625.

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Pandey, Shishir, and Rahul Vaze. "Trustworthiness of t-Distributed Stochastic Neighbour Embedding." In CODS '16: IKDD Conference on Data Science, 2016. ACM, 2016. http://dx.doi.org/10.1145/2888451.2888465.

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Rogovschi, Nicoleta, Jun Kitazono, Nistor Grozavu, Toshiaki Omori, and Seiichi Ozawa. "t-Distributed stochastic neighbor embedding spectral clustering." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966046.

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Mounce, Stephen. "Visualizing smart water meter dataset clustering with parametric t-distributed stochastic neighbour embedding." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393065.

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Zhang, Biyin, and Xin Yu. "Hyperspectral image visualization using t-distributed stochastic neighbor embedding." In Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), edited by Jianguo Liu and Hong Sun. SPIE, 2015. http://dx.doi.org/10.1117/12.2205840.

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Zhu, Ye, and Kai Ming Ting. "Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/812.

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This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. We show that Isolation kernel addresses two deficiencies of t-SNE that employs Gaussian kernel, and the use of Isolation kernel enables t-SNE to deal with large-scale datasets in less runtime without trading off accuracy, unlike existing methods used in speeding up t-SNE.
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Yang, Zan, Yuan Sun, Dan Li, Zhihao Zhang, and Yuchen Xie. "T-Distributed Stochastic Neighbor Embedding with Gauss Initialization of Quantum Whale Optimization Algorithm." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189639.

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Qiu, Mengdie, Zan Yang, Wei Nai, Dan Li, Yidan Xing, and Kai Li. "T-Distributed Stochastic Neighbor Embedding Based on Cockroach Swarm Optimization with Student Distribution Parameters." In 2021 IEEE 12th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2021. http://dx.doi.org/10.1109/icsess52187.2021.9522161.

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