n-dimensional space
n维空间
n-dimensional analysis
n维分析
n-dimensional data
n维数据
n-dimensional model
n维模型
n-dimensional geometry
n维几何
n-dimensional world
n维世界
rendering n-dimensional
渲染n维
exploring n-dimensional
探索n维
defining n-dimensional
定义n维
high n-dimensional
高n维
the dataset was analyzed using n-dimensional scaling to reduce complexity.
数据集使用n维标度法进行分析以减少复杂性。
we explored the data in n-dimensional space to identify potential clusters.
我们在n维空间中探索数据以识别潜在的聚类。
the model represents data points in an n-dimensional feature space.
该模型在n维特征空间中表示数据点。
visualizing data in n-dimensional space can be challenging but insightful.
在n维空间中可视化数据可能具有挑战性，但却很有见地。
the algorithm projects high-dimensional data into a lower n-dimensional representation.
该算法将高维数据投影到较低的n维表示中。
understanding n-dimensional geometry is crucial for advanced data analysis.
理解n维几何对于高级数据分析至关重要。
the researchers used n-dimensional embeddings to capture semantic relationships.
研究人员使用n维嵌入来捕捉语义关系。
we applied principal component analysis to reduce the data to n-dimensional components.
我们将主成分分析应用于将数据减少到n维分量。
the n-dimensional hypercube is a useful concept in topology.
n维超立方体是拓扑学中的一个有用概念。
the system maps users into an n-dimensional preference space.
该系统将用户映射到n维偏好空间。
we need to consider the curse of dimensionality in n-dimensional data.
我们需要考虑n维数据中的维度灾难问题。
n-dimensional space
n维空间
n-dimensional analysis
n维分析
n-dimensional data
n维数据
n-dimensional model
n维模型
n-dimensional geometry
n维几何
n-dimensional world
n维世界
rendering n-dimensional
渲染n维
exploring n-dimensional
探索n维
defining n-dimensional
定义n维
high n-dimensional
高n维
the dataset was analyzed using n-dimensional scaling to reduce complexity.
数据集使用n维标度法进行分析以减少复杂性。
we explored the data in n-dimensional space to identify potential clusters.
我们在n维空间中探索数据以识别潜在的聚类。
the model represents data points in an n-dimensional feature space.
该模型在n维特征空间中表示数据点。
visualizing data in n-dimensional space can be challenging but insightful.
在n维空间中可视化数据可能具有挑战性，但却很有见地。
the algorithm projects high-dimensional data into a lower n-dimensional representation.
该算法将高维数据投影到较低的n维表示中。
understanding n-dimensional geometry is crucial for advanced data analysis.
理解n维几何对于高级数据分析至关重要。
the researchers used n-dimensional embeddings to capture semantic relationships.
研究人员使用n维嵌入来捕捉语义关系。
we applied principal component analysis to reduce the data to n-dimensional components.
我们将主成分分析应用于将数据减少到n维分量。
the n-dimensional hypercube is a useful concept in topology.
n维超立方体是拓扑学中的一个有用概念。
the system maps users into an n-dimensional preference space.
该系统将用户映射到n维偏好空间。
we need to consider the curse of dimensionality in n-dimensional data.
我们需要考虑n维数据中的维度灾难问题。
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