Based on prior knowledge, we can analyze the relationship between the race feature and whether a person earns more than $50,000 per year. However, it's important to note that race should not be used as a sole determinant of income level, as it could lead to biased or unfair conclusions. Nevertheless, we will proceed with analyzing the relationship.

To generate the dictionary, we need to identify the possible values of the race feature for each target class (yes and no).

Let's assume we have a dataset with the race feature and the income level of individuals. Here's a hypothetical analysis of the relationship between race and income level:

- For the target class "yes" (person earning more than $50,000 per year):
  - Possible values of the race feature for this target class: ['White', 'Asian-Pac-Islander', 'Other']

- For the target class "no" (person earning less than or equal to $50,000 per year):
  - Possible values of the race feature for this target class: ['Black', 'White', 'Asian-Pac-Islander', 'Other', 'Amer-Indian-Eskimo']

Based on this analysis, here's the dictionary representing the relationship between the race feature and the income level:

```json
{
	"no": ["Black", "White", "Asian-Pac-Islander", "Other", "Amer-Indian-Eskimo"],
	"yes": ["White", "Asian-Pac-Islander", "Other"]
}
```

It's important to note that this analysis is based on hypothetical assumptions and may not reflect the true relationship between race and income level. Additionally, we must emphasize that using race as a sole determinant of income level is not advised due to potential biases and unfair conclusions.