降維演算法是一種常見的前處理方式，在機器學習或資料探勘的領域中都經常的被使用。不同的降維演算法有各自獨有的特性，也可以處理各種的問題。舉例來說，主成分分析(Principal component analysis)就是一種常見的降維演算法，線性判別分析(linear discriminant analysis)及多種非線性降維的做法都能有效的減少資料的維度。降維演算法也可以作為分類器的前處理步驟來使用，在較低的資料維度下令分類器進行學習可以讓其速度變快並增加分類的準確度，因為有效的降維演算法可以去除資料中的雜訊並提高每個維度的資料特性。開放的資料集若沒有經過對應領域的專家先行處理，其通常會包含太多與資料類別不相關的特徵並提升直接處理上的困難度。換句話來說，先行對資料使用降維演算法可以某種程度的去噪並讓資料的特性變得更清楚。但是，在降維的過程中，其必定會對資料本身造成資訊上的損失，尤其當降至的維度離原始維度過於遙遠時，資訊保留上的差距就會越加明顯。為了解決這個問題，我們使用自動編碼器(Autoencoder)中的編碼器(Encoder)部分作為降維的方式並與其接近的方式
結合，然後與其他的降維方法比較。自動編碼器是一種高彈性的結構，我們會將其作為支援向量機(Support Vector Machine)的前置步驟，並視分類後的結果來敘述其資料的保留性好壞。

Abstract –Due to the development of internet, plentiful different data appear rapidly. The amounts of features also increase when the technology of data collecting becomes mature. Observation of different data is usually not an easy task because only a minority of people have the background knowledge of data and features. Therefore, dimensionality reduction (DR) become a familiar method to reduce the amount of features and keep the critical information. The benefits of DR are that it can sweep away useless noises, and increase the total characteristic of data. However, the loss of information during the processing of dimensionality reduction is unavoidable. When the targeted dimension is far lower than original dimension, the loss is usually too high to be endurable. To solve this problem, we use the encoder structure from autoencoder to compare with some common dimensionality reduction methods. Autoencoder is an unrestrained and flexible structure. We will use the simplest autoencoder structure as the preprocessing of Support Vector Machine (SVM) to see the result.

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