Lsi comes from the field of information retrieval . it transforms the original vector space to abstract k - dimension semantic space . so the huge dimensions of the original vector space are reduced greatly , also the training speed and system performance are improved 系統(tǒng)中引入了信息檢索中的常用技術(shù)? ?潛在語義索引,把原始向量空間轉(zhuǎn)換到抽象的k維語義空間,實現(xiàn)原始向量空間的降維,提高網(wǎng)絡(luò)訓練速度和性能。
It includes 3d semantic space and constraining based problem - solving method . the 3d semantic space is built to represent the relationship between the describing semantic word and product color . then , the product color schemes with related abstract sense words is achieved and saved in database 首先采用定性和定量相結(jié)合的方法,比較各抽象語義詞匯對人心理感覺影響的差異,建立了抽象語言和色彩關(guān)聯(lián)的三維風格語義空間,為色彩信息的表示及推理奠定了基礎(chǔ)。
The document space is generally of high dimensionality and clustering in such a high dimensional space is often infeasible due to the curse of dimensionality . so the primary step in document clustering is to project the document into a lower - rank semantic space in which the documents related to the same semantics are close to each other 基于文本空間的文本聚類因為其具有高維的特征而不容易直接實現(xiàn),所以文本聚類的首要步驟就是將文本空間的數(shù)據(jù)投影到較低維的語義空間里,使在文本空間里相鄰的數(shù)據(jù)向量在語義空間里根據(jù)某些提取的特征參數(shù)而相似。