以二維影像辨識物體時，當欲辨識的物體並非由一個固定的視角成像時，此時特徵空間的資料分佈便會較為複雜。這是由於在特徵空間中，二維影像在不同視角下產生的差異與大量的手形類別數目所造成。本篇論文嘗試利用所提出的「統計型局部線性內嵌法」來辨識多視角之台灣手語手形資料。「局部線性內嵌法」為一非監督式非線性降維方法，它利用局部線性之特性，以獲得內嵌於高維空間中之低維流形；藉由維持鄰近區域之結構，保留住原資料分佈之非線性結構。雖然「局部線性內嵌法」具有在低維空間保持資料非線性結構之能力，但是在將高維資料投影到低維空間的過程，並沒有將類別資訊納入考慮。以鑑於此，本文提出「統計型局部線性內嵌法」，以統計之方式提供類別資訊，以加強「局部線性內嵌法」在處理有關分類之能力。實驗以UCI資料集與台灣手語多視角手形之辨識應用，來驗證「統計型局部線性內嵌法」在分類之效能。實驗結果展現，「統計型局部線性內嵌法」比起原「局部線性內嵌法」獲致更好的分類效能。

The image-based object recognition problem becomes complicated when the objects of interest are not posed at a fixed view. In recognition of sign language, the variation of a hand shape due to multiple views and the large number of hand shapes (classes) yield a data distribution with a complicated nonlinear structure in the feature space. This makes it difficult to preserve the class separability under a linear transformation of dimensionality reduction. The locally linear embedding (LLE) is an unsupervised nonlinear dimensionality reduction approach that utilizes the local linearity to discover the low dimensional manifold embedded in the high dimensional space. This suggests that LLE may preserve the neighborhood configuration for the nonlinear structure of the multi-view hand shape data distribution. Although LLE has capability to recover the global nonlinear structure from locally linear fits, the class label information is not taken into account when mapping samples from the high dimensional space to a low dimensional feature space. The statistical LLE is thus proposed herein to improve the capability of LLE associated with classification by incorporating the class label information statistically. In experiments, the statistical LLE was applied to a multi-view TSL hand shape dataset to reduce dimensionality prior to classification. Several UCI datasets were also utilized to validate the proposed approach. Experimental results show that the statistical LLE gave a superior classification performance compared to the original LLE and the linear dimensionality reduction methods such as LDA and PCA.

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