In the last few years, the m-healthcare applications based on Internet of Things (IoT) have provided multi-dimensional features and real-time services.
This short communication paper introduces a conceptual framework for studying the multi-dimensional features of biocultural diversity in cities along the three key dimensions of materialized, lived and stewardship, being departure points from which biocultural diversity can be studied.
Based on SON_LSII, a mapping method is used to reduce the multi-dimensional features into a single dimension and an efficient indexing algorithm is presented to support complex range queries of the spatial information with a massive number of concurrent users.
Hence, dimensionality reduction is applied to transform the multi-dimensional feature space to two or three numeric features while trying to preserve the data structure [15].
Firstly, a multi-dimensional feature vector is constructed using local spatial information.
Due to the multi-dimensional feature of sustainability, how to account for the impacts of various design factors and the cause-and-effect relationships can be very difficult.
However, some of the recently reported shape representations are embedded in arbitrary metric spaces, i.e. distance spaces, rather than in multi-dimensional feature space.
With GSK, siRNA sequences were represented as vectors in a multi-dimensional feature space according to the numbers of subsequences in each siRNA, and subsequently classified with SVM into effective or ineffective siRNAs.
For shape clustering applications, most existing algorithms assume the shape representations either to be embedded in a multi-dimensional feature space or a metric distance space, making it difficult to evaluate several shape representations that do not conform to these assumptions.
To utilize linear regressions for nonlinear models, nonlinear maps that transform data into a multi-dimensional feature space are engaged.
Non-linear problems are tackled by transforming them into linear ones in multi-dimensional feature space using Kernel functions.
