| 1. |
- Fujiwara, Takanori, et al.
(author)
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Feature Learning for Dimensionality Reduction toward Maximal Extraction of Hidden Patterns
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Other publication (other academic/artistic)abstract
- Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data. One main aim of DR is to reveal hidden patterns that lie on intrinsic low-dimensional manifolds. However, DR often overlooks important patterns when the manifolds are strongly distorted or hidden by certain influential data attributes. This paper presents a feature learning framework, FEALM, designed to generate an optimized set of data projections for nonlinear DR in order to capture important patterns in the hidden manifolds. These projections produce maximally different nearest-neighbor graphs so that resultant DR outcomes are significantly different. To achieve such a capability, we design an optimization algorithm as well as introduce a new graph dissimilarity measure, called neighbor-shape dissimilarity. Additionally, we develop interactive visualizations to assist comparison of obtained DR results and interpretation of each DR result. We demonstrate FEALM's effectiveness through experiments using synthetic datasets and multiple case studies on real-world datasets.
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| 2. |
- Yao, Qingxia, et al.
(author)
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Highly porous isoreticular lanthanide metal-organic frameworks
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Other publication (other academic/artistic)abstract
- As an emerging type of porous materials, metal–organic frameworks (MOFs) have the advantages over conventional inorganic porous materials in that their structures and functions are systematically and predictably designable. Isoreticular expansion is an efficient way for systematic design and control of pore size and shape for MOFs. By using our proposed strategy, a series of highly porous isoreticular lanthanide-based metal-organic frameworks with systematic pore apertures has been obtained, which afford an isoreticular series of MIL-103 structures (termed SUMOF-7I to IV) with pore apertures ranging from 7.2 Å to 23 Å. These materials demonstrated exhibit robust architectures with permanent porosity, and exceptional thermal stability and chemical stability in various solvents. The combination of luminescence property and significant porosity of these MOFs enable them as a potential platform for multifunctional purpose.
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