A more globally accurate dimensionality reduction method using triplets
By Ehsan Amid, Manfred K. Warmuth. 2018
16 citations recorded by the source corpus.
We first show that the commonly used dimensionality reduction (DR) methods such as t-SNE and LargeVis poorly capture the global structure of the data in the low dimensional embedding. We show this via a number of tests for the DR methods that can be easily applied by any practitioner to the dataset at hand. Surprisingly enough, t-SNE performs the best w.r.t. the commonly used measures that reward the local neighborhood accuracy such as precision-recall while having the worst performance in our tests for global structure. We then contrast the performance of these two DR method against our new method called TriMap. The main idea behind TriMap is to capture higher orders of structure with triplet information (instead of pairwise information used by t-SNE and LargeVis), and to minimize a robust loss function for satisfying the chosen triplets. We provide compelling experimental evidence on large natural datasets for the clear advantage of the TriMap DR results. As LargeVis, TriMap scales linearly with the number of data points.
Subfields: Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition
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Citations in the corpus
- Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, background): “It is advisable to note that certain data structures, such as cluster hierarchy, cannot be revealed with t-SNE 43,44 . t-SNE accessibility in cytometry analysis software lead to its not infrequent misuse with cytometry data, evident when the cluster-like structure is not prominent in the map.”
- Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets (2018, background): “It is advisable to note that certain data structures, such as cluster hierarchy, cannot be revealed with t-SNE 40,41 . t-SNE accessibility in cytometry analysis software lead to its not infrequent misuse with cytometry data, evident when the cluster-like structure is not prominent in the map.”