23 research outputs found
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Hyperbolic Ordinal Embedding
Given ordinal relations such as the object i is more similar to j than k is to l, ordinal embedding is to embed these objects into a low-dimensional space with all ordinal constraints
preserved. Although existing approaches have preserved ordinal relations in Euclidean
space, whether Euclidean space is compatible with true data structure is largely ignored,
although it is essential to effective embedding. Since real data often exhibit hierarchical
structure, it is hard for Euclidean space approaches to achieve effective embeddings in low
dimensionality, which incurs high computational complexity or overfitting. In this paper we
propose a novel hyperbolic ordinal embedding (HOE) method to embed objects in hyperbolic space. Due to the hierarchy-friendly property of hyperbolic space, HOE can effectively
capture the hierarchy to achieve embeddings in an extremely low-dimensional space. We
have not only theoretically proved the superiority of hyperbolic space and the limitations
of Euclidean space for embedding hierarchical data, but also experimentally demonstrated
that HOE significantly outperforms Euclidean-based methods
Generalization error bound for hyperbolic ordinal embedding
38th International Conference on Machine Learning, 18-24 July 2021, Virtual202210 bcchVersion of RecordSelf-fundedPublishe
Generalization bounds for graph embedding using negative sampling : linear vs hyperbolic
35th Conference on Neural Information Processing Systems (NeurIPS 2021), Virtual-only Conference, 6-14 Dec, 2021202302 bcchAccepted ManuscriptOthersJST, JST-AIP, Daiwa Foundation Award, JST-PRESTOPublishe
Systematic Expression Profiling of the Mouse Transcriptome Using RIKEN cDNA Microarrays
The number of known mRNA transcripts in the mouse has been greatly expanded by the RIKEN Mouse Gene Encyclopedia project. Validation of their reproducible expression in a tissue is an important contribution to the study of functional genomics. In this report, we determine the expression profile of 57,931 clones on 20 mouse tissues using cDNA microarrays. Of these 57,931 clones, 22,928 clones correspond to the FANTOM2 clone set. The set represents 20,234 transcriptional units (TUs) out of 33,409 TUs in the FANTOM2 set. We identified 7206 separate clones that satisfied stringent criteria for tissue-specific expression. Gene Ontology terms were assigned for these 7206 clones, and the proportion of `molecular function' ontology for each tissue-specific clone was examined. These data will provide insights into the function of each tissue. Tissue-specific gene expression profiles obtained using our cDNA microarrays were also compared with the data extracted from the GNF Expression Atlas based on Affymetrix microarrays. One major outcome of the RIKEN transcriptome analysis is the identification of numerous nonprotein-coding mRNAs. The expression profile was also used to obtain evidence of expression for putative noncoding RNAs. In addition, 1926 clones (70%) of 2768 clones that were categorized as “unknown EST,” and 1969 (58%) clones of 3388 clones that were categorized as “unclassifiable” were also shown to be reproducibly expressed