An object can be consisting of various attributes, such as illuminance, appearance, shape, orientation, etc. Separately extract these attributes has enormous value in visual effects modeling, attribute-specific retrieval and recognition. Essentially, these attributes can be fairly abstract and thus need labels to extract. However, sometimes the labels of these attributes may not be available with training data. A solution to this problem is projecting the observed data into a lower dimension latent subspace, such that each observed data can be represented by a latent variable. After that, the dimensions of a latent variable can be segmented into different parts by weighting the kernel automatic relevance determination (ARD) parameters. Consequently, the latent variable is segmented into different parts each of which corresponds to the main attribute. In real life scenery, the attributes of an object may vary significantly from case to case. For instance, a single face can probably be under different illuminance conditions. Taking into account the diversity of these attribute variations, we propose the Diversified Shared Latent Variable Model (DSLVM) to extract and manipulate object attributes in an unsupervised way. More specifically, we initially set up two views that share the same latent variables. Then, two Diversity Encouraging (DE) priors are applied to the inducing points of each model view. Here, the inducing points are a small representative dataset that explains the observed data in its entirety. Meanwhile, the exploited diversity encouraging priors are able to cover more diverse characteristics of the attributes. The defined objective function is computed by variational inference. Extensive experiments on different datasets demonstrate that our method can accurately deal with various object.